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This is a continuation of application Ser. No. 08/369,034 filed on Jan. 4, 1995 now abandoned.
parsing said natural language input;
disambiguating meanings and structure of said natural language input;
generating a discourse representation, said discourse representation identifying said structure for said natural language input, meanings of words and relationships between components of said structure;
resolving references in said natural language input;
determining relationships that exist in said natural language input;
consulting, within each of the above steps, a naive semantic lexicon to determine the plausibility of interpretative decisions made within each of the above steps, said naive semantic lexicon having at least one entry that identifies at least one sense of a word, said entry including a reference to an ontological classification network, syntactic information, and a plurality of semantic properties.
parsing a word in said natural language input; and
analyzing said word relative to other words in said natural language input.
eliminating structural ambiguity in said natural language input; and
eliminating word sense ambiguity in said natural language input.
identifying an entry in a dictionary for a word contained in said natural language input;
identifying a plurality of common sense meanings of said word contained in said natural language input;
identifying syntactic information associated with each of said plurality of common sense meanings; and
identifying semantic information associated with said each of said plurality of common sense meanings.
generating a partial discourse representation for a subset of said natural language input; and
adding said partial discourse representation to an overall discourse representation for said natural language input.
inputting said query's cognitive model to a Relevance Reasoning Module; and
generating, using said Relevance Reasoning Module, a short list of relevant text.
inputting textual information to a natural language understanding module;
translating, using said natural language understanding module, said textual information into a logic form which expresses objects and events in said textual information as concepts and relationships between said concepts that identify the structure expressed in said textual information;
inputting a query to said natural language understanding module;
translating, using said natural language understanding module, said query into a logic form which expresses objects and events in said query as concepts and relationships between said concepts that identify the structure expressed in said query;
comparing said logic form of said query to said logic form of said textual information; and
retrieving said textual information when said query's logic form is similar to said textual information's logic form.
storing textual information in storage of said computer system;
inputting said textual information to a natural language understanding (NLU) module, said NLU module comprising parsing, disambiguation, formal semantics, anaphora resolution and coherence modules that consult a naive semantic lexicon having at least one entry that identifies at least one sense of a word, said entry including a reference to an ontological classification network, syntactic information and a plurality of semantic properties;
generating, using said natural language understanding module, a cognitive model which expresses objects and events in said textual information as concepts and relationships between said concepts that identify the structure expressed in said textual information; and
generating a concept index of said concepts in said cognitive model, said concept index is used to locate the textual information associated with concepts in said cognitive model.
storing in said computer system textual information;
generating, using a natural language understanding module comprising parsing, disambiguation, formal semantics, anaphora resolution and coherence modules consulting a naive semantic lexicon having at least one entry that identifies at least one sense of a word, said entry including a reference to an ontological classification network, syntactic information and a plurality of semantic properties, a cognitive model of said textual information which expresses objects and events in said textual information as concepts and relationships between said concepts that identify the structure expressed in said textual information;
generating, using said natural language understanding module, a cognitive model of a query which expresses objects and events in said query as concepts and relationships between said concepts that identify the structure expressed in said query; and
generating a list of textual information relevant to said query based on said cognitive model of said textual information and said cognitive model of said query.
inputting said query's cognitive model to a High Recall Statistical Retrieval (HSRS) Module of said computer system;
generating, using said HSRS Module, a long list of relevant text;
inputting said long list of relevant text to a Relevance Reasoning Module; and
generating, using said Relevance Reasoning Module, a short list of relevant text.
selecting an entry in said long list;
retrieving, from said textual information's cognitive model, a cognitive model of text associated with said entry;
comparing said text's cognitive model with said cognitive model of said query; and
selecting said entry for said short list when said text's cognitive model is similar to said query's cognitive model.
identifying truth conditions asserted in said text;
identifying truth conditions asserted in said query; and
determining deductively whether said text's truth conditions conform to said query's truth conditions.
a computer usable medium having computer readable program code embodied therein for interpreting natural language input comprising:
computer readable program code configured to cause a computer to parse said natural language input driven by the content of said natural language input and expectations about said natural language input using a naive semantic lexicon having at least one entry that identifies at least one sense of a word, said entry including a reference to an ontological classification network, syntactic information, and a plurality of semantic properties;
computer readable program code configured to cause a computer to disambiguate during said parse said natural language input using said naive semantic lexicon;
computer readable program code configured to cause a computer to generate a discourse representation said natural language input using said naive semantic lexicon;
computer readable program code configured to cause a computer to resolve references in said natural language input using said naive semantic lexicon; and
computer readable program code configured to cause a computer to determine relationships that exist in said natural language input using said naive semantic lexicon.
computer readable code configured to cause a computer to parse a word in said natural language input; and
computer readable code configured to cause a computer to analyze said word relative to other words in said natural language input.
computer readable code configured to cause a computer to eliminate structural ambiguity in said natural language input; and
computer readable code configured to cause a computer to eliminate word sense ambiguity in said natural language input.
computer readable code configured to cause a computer to generate a partial discourse representation for a subset of said natural language input; and
computer readable code configured to cause a computer to add said partial discourse representation to an overall discourse representation for said natural language input.
computer readable code configured to cause a computer to translate said natural language input into a discourse representation structure; and
computer readable code configured to cause a computer to translate said discourse representation structure into a logical form.
1. Field of the Invention
This invention relates to natural language interpretation using a computer system.
2. Background Art
Communication with computer systems is accomplished with the use of binary code sequences (so called "machine language") that the computer processor can interpret as instructions. It is difficult to communicate in binary code, so artificial programming languages have been created to make it easier to communicate with a computer system. A programming language for a computer system is a language that can be interpreted or translated into binary code or into a language that itself can ultimately be translated into binary code. Examples are "C", "Basic", "Pascal", "Fortran", etc. Artificial languages have strict rules of syntax, grammar, and vocabulary. Variations from the rules can result in error in communicating with the computer system.
The prior art has attempted to create new, so called "high level" artificial languages that are more like a "natural" language. A natural language is a language, such as English, used by humans to communicate with other humans. However, such high level artificial languages still include strict rules and limitations on vocabulary.
Other attempts have been made to provide a means to interpret communication in natural languages and provide translations of these communications to the computer system for processing. In this manner, a human user could communicate commands or requests to a computer system in English, and the communication could be translated into machine language for use by the computer system. Such attempts are referred to as computerized natural language understanding systems.
Computerized natural language understanding could be used to interpret a query that a user provides to the computer in a natural language (e.g., English). One area where the computerized ability to interpret language could be used is to retrieve text from a text retrieval system based on a human language query.
Existing text retrieval systems store texts (i.e., a document base) and provide a means for specifying a query to retrieve the text from the document base. Prior art text retrieval systems use several types of approaches to provide a means for entering a query and for retrieving text from the document base based on the query.
One approach, a statistical approach, implements a keyword-based system. In this approach, Boolean (i.e., logical) expressions that consist of keywords are used in the query. This approach uses a directory that consists of keywords and the locations of the keywords in the document database. For example, this approach uses a query such as the following that consists of keywords delimited by Boolean operators (the keywords are italicized and the Boolean operators are capitalized):
(attack or change) AND (terrorist OR guerrilla) AND (base OR camp OR post)
The keywords input as part of the query are compared to the keywords contained in the directory. Once a match is found, each location contained in the directory that is associated with the matched keyword can be used to find the location of the text (i.e., keyword) in the document database.
Text retrieval systems are evaluated by two measures, precision and recall. Recall measures the ratio between the number of documents retrieved in response to a given query and the number of relevant documents in the database. Precision measures the ratio between the number of relevant documents retrieved and the total number of documents retrieved in response to a given query. All commercially available and research text retrieval systems perform poorly on both measures. The best research systems do not reach 40% precision and 40% recall. Thus there is built-in tradeoff between recall and precision in keyword-based systems. However, it cannot retrieve text based on the actual meaning and subject content of the documents, so that any texts using different words with the same meaning will not be retrieved. On the other hand, texts using the same words in different meanings will be erroneously retrieved.
Furthermore, the keyword-based approach (employed in all commercial systems) has a built-in tradeoff between precision and recall. When the keyword-based approach has good recall, precision is poor. On the other hand, when precision is good, recall is poor. Thus, when it has good recall, it retrieves many or all of the documents which are relevant to the query, but it also retrieves many others which are irrelevant, and the user has to waste time inspecting many unwanted documents. On the other hand, when precision is good, many of the relevant documents are not retrieved, so that the user cannot be confident that all or enough of the desired information has actually been retrieved and displayed in response to the query.
The reason for poor precision is that the keyword-based approach inspects only surface forms (words and their locations relative to each other in the text), and assumes that these surface features accurately reflect meaning and content. But words are ambiguous and mean different things in context.
In the query above, the words "charge" and "attack" have many meanings, both as nouns and verbs, in English. Similarly, "base", "camp" and "post" are ambiguous. In a document database, they can occur in many texts which have nothing to do with terrorist attacks. Here are some sample irrelevant texts which a keyword-based system would erroneously retrieve in response to the above query:
The ambassador suffered a heart attack directly after his speech at the army base denouncing the guerrillas.
The base commander charged the guerrilla group with treaty violations.
On the other hand, the reason for poor recall is that natural language affords literally thousands of ways of expressing the same concept or describing the same situation. Unless all of the words which could be used to describe the desired information are included in the query, all of the documents can't be retrieved. The reason poor recall systematically results in the context of good precision is that to the extent that the keyword-based system retrieves precisely, the query has enough keywords in it to exclude many of the irrelevant texts which would be retrieved with fewer keywords. But by the same token, and by the very nature of the technology, the addition of keywords excludes a good number of other retrievals which used different combinations of words to describe the situation relevant to the query. For example, the query above would miss relevant texts such as:
The guerrillas bombed the base. The guerrillas hit the base. The guerrillas exploded a bomb at the base. The terrorists bombed the base. The terrorists hit the base. The terrorists exploded a bomb at the base.
This approach has the further disadvantage of using a query language that consists of keywords separated by Boolean operators. A user has difficulty understanding this query structure. Further, it is difficult for a user to predict which words and phrases will actually be present in a relevant document.
Several improvements have been attempted for keyword-based approach. Examples of such improvements include the use of synonym classes, statistical ranking of texts, fuzzy logic, and concept clustering. However, none of these provide any significant improvement in the precision/recall tradeoff. Further, none of these approaches provides a solution to the difficulty with using a Boolean query language.
Another approach implements a semantic network to store word meanings. The basic idea is that the meaning of a word (or concept) is captured in its associated concepts. The meaning is represented as a totality of the nodes reached in a search of a semantic net of associations between concepts. Similarity of meaning is represented as convergence of associations. The network is a hierarchy with "isa" links between concepts. Each node is a word meaning or concept. By traversing down through a hierarchy, a word meaning (or concept) is decomposed.
For example, within one branch of a hierarchy, an ANIMAL node has a child node, BIRD, that has a child node entitled CANARY. This hierarchy decomposes the meaning of the ANIMAL concept into the BIRD concept which is further decomposed into a CANARY. Properties that define a concept exist at each node in the hierarchy. For example, within the ANIMAL branch of the hierarchy, the BIRD node has "has wings" and "can fly" properties. The CANARY node has "can sing" and "is yellow" properties. Further, a child node inherits the properties of its ancestor node(s). Thus, the BIRD node inherits the properties of the ANIMAL, and the CANARY node inherits the properties of the BIRD and ANIMAL nodes.
The semantic net idea is an important one in artificial intelligence and some version of a classification scheme is incorporated in all semantic representations for natural languages. However, in these prior versions, the classification scheme is: 1) not tied to the specific meanings (senses) of words, 2) not based upon psycholinguistic research findings, 3) not integrated with syntactic information about word senses, and 4) not deployed during parsing.
Furthermore, it has been assumed, in prior approaches, that word meanings can be decomposed into a relatively small set of primitives, but it has been shown that lexical knowledge is not limited to a finite set of verbal primitives. Most importantly, no knowledge representation scheme for natural language, including any semantic net representation, has been able to overcome the apparent intractability of representing all of the concepts of a natural language. Small examples of semantic nets were created, but never a large enough one to provide the basis for a natural language system.
Another type of network approach, neural net, attempts to simulate the rapid results of cognitive processes by spreading the processing across a large number of nodes in a network. Many nodes are excited by the input, but some nodes are repeatedly excited, while others are only excited once or a few times. The most excited nodes are used in the interpretation. In the model, the input is repeatedly cycled through the network until it settles upon an interpretation.
This method substitutes the difficult problem of modeling human parsing on a computer with modeling language learning on a computer. Researchers have known and formalized the parsing rules of English and other natural languages for years. The problem has been the combinatorial explosion. This method provides no new insights into parsing strategies which would permit the efficient, feasible application of the rules on a computer once the system has automatically "learned" the rules. Nor has any such system as yet learned any significant number of rules of a natural language.
Another approach, a "traditional" approach, attempts to interpret language in a manner that more closely parallels the way a human interprets language. The traditional approach analyzes language at a number of levels, based on formal theories of how people understand language. For example, a sentence is analyzed to determine a syntactic structure for the sentence (i.e., the subject, verb and what words modify others). Then a dictionary is used to look up the words in the sentence to determine the different word senses for each word and then try to construct the meaning of the sentence. The sentence is also related to some knowledge of context to apply common sense understanding to interpret the sentence.
These approaches, which were the main focus of research in the 1970's and 1980's have not worked in the past, for two main reasons. First, the traditional approach creates a combinatorial explosion of analyses. There are too many ways to analyze a sentence and too many possible meanings for the words in the sentence. Because of this combinatorial explosion, a simple sentence can take hours or days to process. Current implementations of the traditional approach have no means for blocking the combinatorial explosion of the analysis. Further, there is no adequate ability to reason about the feasible meaning of the sentence in context.
As a result of these problems, leading experts in the field of computational linguistics have abandoned the traditional approach. For example, Eugene Charniak of Brown University abandons the traditional approach in favor of a statistical approach and states, in his 1993 book STATISTICAL LANGUAGE LEARNING (Cambridge, Mass. MIT Press). He states:
"I think it is fair to say that few, if any, consider the traditional study of language from an artificial intelligence point of view a `hot` area of research . . . language comprehension from an AI point of view assumes that language understanding depends on a lot of "real-world knowledge" and that our programs must have it if they are ultimately to succeed."
In support of his decision to abandon the traditional approach in favor of a statistical approach, Eugene Charniak further states:
"There is nothing wrong with the traditional approach! as far as it goes. But at the same time anyone familiar with artificial intelligence! must realize that the study of knowledge representation--at least as it applies to the "commonsense" knowledge required for reading typical texts such as newspapers --is not going anywhere fast. . . Thus many of us in artificial intelligence-natural language processing! have found ourselves in the position of basing our research on the successful completion of other's research--a completion that is looking more and more problematic. It is therefore time to switch paradigms."
Thus, none of the above approaches provide an ideal system that allows an individual to state a query in a natural language (i.e., English) in much the same way that the individual would pose a question to or interface with an associate. Further, none of the above approaches provides an optimal combination of recall and precision in a text retrieval such that the maximum number of documents that provide an accurate response to a text retrieval query are retrieved. None of the above approaches has been able to control the combinatorial explosion of analyses. None of the above approaches has been able to surmount the intractability of common sense reasoning.
The present invention provides the ability for a computer system to interpret natural language input. It avoids the combinatorial explosion that has been an obstacle to natural language interpretation. Further, the present invention uses common sense knowledge (or world knowledge) to interpret natural language input.
The present invention uses modules for parsing, disambiguation, formal semantics, anaphora resolution and coherence, and a naive semantic lexicon to interpret natural language input. The naive semantic lexicon is consulted by the other modules to determine whether an interpretation alternative is plausible, that is, whether the interpretation alternative makes sense based on the world knowledge contained in the naive semantic lexicon. By eliminating the implausible alternatives at each decision point in the interpretation, the potentially unlimited combinations of alternatives can be eliminated thereby avoiding the combinatorial explosion that has occurred in past attempts at natural language interpretation.
The use of naive semantics is crucial at all levels of analysis, beginning with the syntax, where it is used at every structure building step to avoid combinatorial explosion. The key idea of this approach to semantics is that people rely on superficial, commonsense knowledge when they speak or write. That means that understanding should not involve complex deductions or sophisticated analysis of the world, but just what is immediately "obvious" or "natural" to assume. This knowledge often involves assumptions (sometimes "naive" assumptions) about the world, and about the context of the discourse.
The present invention uses a naive semantic ontology which is a sophisticated semantic net. The ontology provides a means for classifying basic concepts and interrelationships between concepts. The classification system used by the present invention provides psychologically motivated divisions of the world. A dictionary (lexicon) relates word senses to the basic ontological concepts and specifies common sense knowledge for each word sense. The dictionary connects syntactic information with the meaning of a word sense. The lexicon is unique in its integration of syntactic facts, ontological information and the commonsense knowledge for each sense of each word. Further, the lexicon is unique because it is psychologically motivated.
Text retrieval provides one application of the natural language interpretation capability of the present invention. Feasible text retrieval is based on the "understanding" of both the text to be retrieved and the request to retrieve text (i.e., query). The "understanding" of the text and the query involve the computation of structural and semantic representations based on morphological, syntactic, semantic, and discourse analysis using real-world common sense knowledge.
The interpretative capabilities of the present invention are used in two separate processes. The first process uses the natural language understanding (NLU) module of the present invention to "digest" text stored in a full text storage and generate a cognitive model. The cognitive model is in first order logic (FOL) form. An index to the concepts in the cognitive model is also generated, so that the concepts can be located in the original full text for display.
A second process interprets a query and retrieves relevant material from the full text storage for review by the requester. The NLU module is used to generate a cognitive model of a text retrieval request (i.e., query). The cognitive model of the text and the cognitive model of the query are compared to identify similar concepts in each. Where a similar concept is found, the text associated with the concept is retrieved. Two passes (i.e., a high recall statistical pass and a relevance reasoning pass) are used to generate a short list of documents that are relevant to the query. The short list is ranked in order of relevance and is displayed to the user. The user selects texts and browses them in a display window.
FIG. 1 illustrates a general purpose computer for implementing the present invention.
FIG. 2 provides an overview of a text retrieval system that uses the natural language understanding capabilities of the present invention.
FIG. 3 illustrates a model generation and information classification architecture.
FIG. 4 provides an illustration of a Text Retrieval Architecture.
FIG. 5 provides an overview of the NLU module.
FIGS. 6A-6B provide examples of a Discourse Representation Structure.
FIG. 7 illustrates an ontology that can be used to classify word senses in the English language.
FIG. 8 illustrates a dictionary entry used in the present invention.
The present invention was made with Government support under Contract No. DAAA15-92-C-0025 awarded by the United States Department of the Army. The Government has certain rights in the invention.
A Natural Language Interpretation System is described. In the following description, numerous specific details are set forth in order to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.
The present invention can be implemented on a general purpose computer such as illustrated in FIG. 1. A keyboard 110 and mouse 111 are coupled to a bi-directional system bus 118. The keyboard and mouse are for introducing user input to the computer system and communicating that user input to CPU 113. The computer system of FIG. 1 also includes a video memory 114, main memory 115 and mass storage 112, all coupled to bi-directional system bus 118 along with keyboard 110, mouse 111 and CPU 113. The mass storage 112 may include both fixed and removable media, such as magnetic, optical or magnetic optical storage systems or any other available mass storage technology. Bus 118 may contain, for example, 32 address lines for addressing video memory 114 or main memory 115. The system bus 118 also includes, for example, a 32-bit DATA bus for transferring DATA between and among the components, such as CPU 113, main memory 115, video memory 114 and mass storage 112. Alternatively, multiplex DATA/address lines may be used instead of separate DATA and address lines.
In the preferred embodiment of this invention, the CPU 113 is a 32-bit microprocessor manufactured by Motorola, such as the 680X0 processor or a microprocessor manufactured by Intel, such as the 80X86, or Pentium processor. However, any other suitable microprocessor or microcomputer may be utilized. Main memory 115 is comprised of dynamic random access memory (DRAM). Video memory 114 is a dual-ported video random access memory. One port of the video memory 114 is coupled to video amplifier 116. The video amplifier 116 is used to drive the cathode ray tube (CRT) raster monitor 117. Video amplifier 116 is well known in the art and may be implemented by any suitable means. This circuitry converts pixel DATA stored in video memory 114 to a raster signal suitable for use by monitor 117. Monitor 117 is a type of monitor suitable for displaying graphic images.
The computer system described above is for purposes of example only. The present invention may be implemented in any type of computer system or programming or processing environment.
The present invention is described below in relation to a text retrieval system. However, the present invention can be used with other applications to provide a human interface between the computer and the user or simulate human language interpretation. For example, the present invention can be used to automatically understand and interpret a book and generate an abstract for the book without human intervention. The present invention can be used to provide an interface to the Worldwide Web and the Information Highway.
Further, the present invention can be used to develop a natural language interface to a computer system such that a user can command a computer, robot, or write computer programs in a natural language. The present invention can be used to provide the ability for a robot to behave independently on the basis of world knowledge. Computers with the natural language capabilities of the present invention can begin to learn the environment just as a child does.
The present invention understands a natural language (e.g., English) in a way which is similar to human understanding. A natural language is both highly ambiguous (the same pattern can mean many different things), and redundant (the same meaning can be expressed with many different patterns). The present invention uses a Natural Language Understanding (NLU) module to analyze this complex structure, and unravel its meaning layer by layer. The NLU module receives a natural language input and generates a first order logic (FOL) output.
The NLU module blocks the combinatorial explosion that has occurred in the prior attempts to parse and understand natural language on a computer. The combinatorial explosion results from the many possible structures and meanings that can be given to words and phrases in a natural language. Further, the NLU module avoids the intractability of common sense reasoning. These problems have caused past attempts at interpreting natural languages to fail, as Charniak stated. The NLU module eliminates these problems and provides accurate interpretations. FIG. 5 provides an overview of the NLU module.
Parser 502 analyzes the grammatical parts of a natural language sentence or discourse and their roles relative to each other. For example, parser 502 identifies the noun, verb, etc. and determines what phrases modify what other portions (e.g., noun phrase or verb phrase) of the sentence.
In the invention, a left-corner head-driven parsing strategy is used. This parsing strategy mixes a top-down syntactic analysis strategy with a bottom-up syntactic analysis strategy. In a bottom-up strategy, the syntactic analysis is driven by the data (e.g., words or phrases) that is currently being processed. In a top-down strategy, the analysis is driven by expectations of what the data must be in order to conform to what is already known from the data previously processed. The advantage of this method is that you can have some expectations about what has not yet been heard (parsed) and can allow the expectations to be tempered (bottom up) by what is actually being heard (parsed). This strategy preserves some of the advantages of a top-down analysis (reducing memory requirements, integrating structure early in the analysis), while still avoiding some of the indeterminacy of a purely top-down analysis.
The mixed parsing strategy just described still faces a combinatorial explosion. Some of this comes from the indeterminacy just noted. That problem can be avoided by memorizing completed parts of the structure.
The disambiguation module 504 is embedded into the parser to avoid the extra work of pursuing unlikely parse pathways. As each structure is built, a naive semantic lexicon 512 is consulted to determine the semantic and pragmatic plausibility of each parsing structure. The naive semantic lexicon 512 contains a knowledge base that identifies word senses that fit within the context of the input being parsed.
The disambiguation module 504 eliminates structural and word sense ambiguity. Structural ambiguity is introduced in at least four ways: noun-verb ambiguity, prepositional phrases, conjunctions, and noun-noun combinations.
Many words have a noun-verb ambiguity and this ambiguity is a major source of the combinatorial explosion. Using naive semantic reasoning, the invention selects the most plausible part of speech for such ambiguous words. For example, the following sentence contains ambiguities:
Face places with arms down.
The words "face", "places" and "arm" in this sentence can be either a noun or a verb. The two words "face places" could form: 1) a noun-noun combination meaning "places for faces"; 2) a noun-verb combination with "face" meaning "pride" as in "save face", and place meaning the verb to "locate in a social status hierarchy; or 3) a verb-noun combination with "face" meaning command to place one's body with the one's face towards something and "places" a noun meaning seating positions at a table.
The present invention is able to select the third option as the most plausible interpretation for the phrase and the most plausible part of speech for each word in context. It selects a verb for "face" and a noun for "places" and "arms" in context because disambiguation reasoning finds that "with arms down" is a plausible modifying phrase for the verb "face" in the sense of position one's body. That choice carries with it the choice that "places" is a noun because noun for "places" is the only possibility if "face" is a verb.
Sentences with prepositional phrases after the object of the verb are ambiguous, because the first prepositional phrase after the object can modify the object, the verb, or the sentence constituent. The present invention provides a computational method for prepositional phrase disambiguation using preposition-specific rules, syntax and naive semantics. This is further described below under "Naive Semantic Reasoning".
Conjunctions (e.g., "and", "but", "as", and "because") serve to connect words, phrases, clauses, or sentences, for example. Conjoined noun phrases and verb phrases create a number of possible interpretations.
For example, in the sentence
The battery and line charged.
only one or two interpretations are plausible. However, the combinations of potential noun senses of "battery" and "line" and verb senses of "charge" amounts to 7*13*8 or 728 interpretations. Using the naive semantic lexicon 512, the disambiguation module 504 first reasons that only a few combinations of the two nouns "battery" and "line" are plausible. Two of the pairs are: 1) a battery of soldiers and a line of soldiers; and 2) the electrical device and the wire. Then, upon considering these pairs as subject of the verb "charge", the disambiguation module 504 selects the pair meaning soldiers, because the naive semantic lexicon 512 has sufficient common sense knowledge to exclude and reject "line" as subject of "charge". The appropriate meaning of "charge" does not accept a kind of wire as subject.
Noun-noun combinations such as "guerrilla attack" or "harbor attack" combine two or more nouns. These two examples, however, illustrate that each combination can create different meanings to the word "attack". For example, in the noun-noun combinations "guerrilla attack" and "harbor attack", "guerrilla" is the underlying subject of a sentence in which guerrillas attack and "harbor" is the object of the attack and the agent is not expressed. Naive semantic reasoning is used to disambiguate noun-noun combinations.
The other type of ambiguity is word sense ambiguity. Word sense ambiguity stems from the many possible meanings that a natural language places on each word in the language. The actual meaning is determined based on its use in a sentence and the meanings given to the other words during the interpretation process. To disambiguate a word, the present invention first uses syntactic clues. Then the present invention consults the naive semantic lexicon 512 to determine whether a possible sense of a word is reasonable given its context in the input being interpreted.
In the Formal Semantics module 506, the meaning of the natural language input is represented in a formal mathematical, logical language. The formal semantics module 506 translates natural language input into a logical form such as first order logical form.
In the formal semantics module 506, a sentence or discourse is translated into a discourse representation structure (DRS). In sentential semantic translation, a sentence is translated into a sentence-specific DRS and incorporates the effects of operators, such as negation, which can alter the interpretation. The sentence-specific DRS is then added to the overall DRS in discourse semantic translation. FIG. 6 provides an example of a DRS for a first sentence of a discourse:
"The base was attacked by the guerrillas today."
The DRS of FIG. 6 contains a list of indexed entities 604 that identify objects and events in the above sentence. For example, "e1" is the event of "attacking" and "the1" and "the2" represent the "guerrilla" and "base2" entities, respectively. The DRS further contains first-order representations 604, 606, 608, and 610 that depict properties and relations expressed in the sentence. For example, representation 604 indicates that event "e1" (i.e., attacking) is the event in which "the1 ("guerrilla") relates to entity "the2" (i.e., "base2") in an attacking event in which "the1" is the attacker and "the2" is the attackee. Representation 610 indicates that event "e1" occurred "today". Notice that words are replaced by concepts: "attack3" is the physical attack concept expressed by the "word attack" and "base2" is the concept of a military base.
The formal semantics module 506 generates output (e.g., DRS) that conveys the truth-conditional properties of a sentence or discourse. That is, the truths or negatives that are meant to be believed by the communication of the sentence or discourse. Later, when the system interprets a query and tries to retrieve texts which are relevant to the query, the relevance reasoning module 412 is used deductively to determine whether the truth conditions asserted in the sentence or discourse conform to the truth conditions contained in a query. In other words, using deduction, the relevance reasoning module 412 determines whether the world of some text in the document database conforms to the world of the query. The use of deduction makes this computation feasible and speedy.
A DRS is translatable into FOL. The FOL is then preferably translated into a programming language such as PROLOG, or a computational knowledge base. By translating the FOL into a programming language, standard programming methods can be applied in the relevance reasoning module 412.
Translation to a "logical form" suitable for reasoning, and also into discourse structures is appropriate for determining the possible antecedents of anaphors and descriptions. The published "discourse representation theory" (DRT) model and structures of Kamp and Asher are adapted for use here.
In the anaphora resolution module 508, entities are tracked and equated as they are mentioned in a sentence or discourse. Anaphora resolution module 508 links pronouns (e.g., he, she, and they) and the noun to which they refer. For example, the following provides an illustration of a discourse that includes the sentence previously illustrated (sentence S1) and a second sentence (S2):
S1: "The base was attacked by guerrillas today."
S2: "They charged the outpost at dawn." FIG. 6B illustrates a modified DRS that includes sentences S1 and S2 of the discourse. With the addition of sentence S2 in the discourse, additional objects and events are added to representation 602. For example, "they1" is added to refer to the object "they", "e2" to refer to the verb "charged", "the3" to refer to "outpost" and g1 to refer to "dawn". First Order representations 612, 614, 616, 618, and 620 are added for this sentence. The anaphora resolution module 508, for example, resolves the occurrence of "they" in the second sentence and equates it to "the1" (i.e., guerrillas) in representation 620. The naive semantic reasoning module 512 is consulted to determine whether guerrillas would charge an outpost as stated in sentence S2, for example.
The coherence module 510 determines the parts of the sentence or discourse that cohere or relate. The coherence module 510 determines the relationships that exist in the natural language input. For example, the coherence module 510 identifies a causal relationship. A causal relationship may exist, for example, in text such that a first portion of text provides the cause of something that occurs later in the text . Another relationship is an exemplification relationship. Such a relationship exists between two text segments where one further provides an example of the other. Goal and enablement are other examples of relationships that can be recognized by the coherence module 510.
The coherence module 510 builds a coherent model of a "world" based on the interpretation of the natural language input. The coherence module 510 uses the naive semantic reasoning module 512 to determine whether a coherence alternative is plausible. Using sentences S1 and S2 in the previous discourse illustration, for example, an inference can be made that "e1" ("attacked") and "e2" ("charged") cohere such that event "e2" occurred as part of event "e1". That is, the act of charging occurred during the attacking event. Thus, the naive semantic reasoning module 512 can be used to determine that one event is broader than the other such that the latter occurred as part of the former. The present invention identifies a subevent of another event as a "constituency". Using the same discourse illustration, the charging event ("e2") is a constituent of the attack event ("e1) is represented as:
"constituency (e1, e2)"
As indicated above, the naive semantic lexicon 512 is consulted in the parser module 502, disambiguation module 504, formal semantics module 506, anaphora resolution module 508, and the coherence module 510 to bring common sense or world knowledge to bear in the decisions on structure and meaning made by a module. The naive semantic lexicon module 512 provides knowledge that is used to allow the NLU module to reason about the likely situation to which the words in a natural language input might be referring.
To eliminate the combinatorial explosion, the naive semantic lexicon 512 is consulted during each segment of the NLU module. In the parser 502, for example, the naive semantic lexicon 512 is consulted whenever parser 502 wishes to make a structural assumption about the natural language input (e.g., to connect a phrase or word to another phrase or word). At each of these decision points, the disambiguation module 504 consults the naive semantic lexicon 512 to assist in determining whether a disambiguation alternative is plausible.
The naive semantic lexicon 512 brings common sense (world knowledge) to bear on the interpretation performed by the NLU module. Common sense provides the ability to eliminate implausible or nonsensical structures or interpretations of the natural language input. The following two sentences serve to illustrate how common sense can be used during interpretation:
"John bought the lock with the money."
"John bought the lock with the key."
To interpret the first sentence, one possible alternative is to connect the prepositional phrase "with the money" to the verb "bought." Another possible alternative is that the phrase "with the money" modifies "lock". When a person hears this sentence, he knows that a lock does not normally come packaged with money. He can, therefore, apply common sense to rule out the second alternative. Based on his common sense, he can determine the meaning of the first sentence to be that someone paid cash to purchase a lock.
Further, while there are several meanings for the word lock (e.g., a security device or an enclosed part of a canal), a person can again apply common sense to select the meaning of the word that would most likely be the intended meaning. In this case, the person would pick the meaning that is most common (i.e., a security device). Therefore, the person uses common sense to interpret the sentence to mean that someone named John paid cash to purchase a security device.
Similarly, an individual can use common sense to connect words and phrases in the second sentence. In this case, the individual's prior knowledge would indicate that a lock is usually not bought using a key as tender. Instead, the individual would connect the "lock" and "with a key". A common sense meaning of a security device can be assigned to the word "key". Thus, the sentence is interpreted to mean that someone named "John" purchased both a security device and a key that can unlock this device.
A person's knowledge base provides the criteria used to interpret the world. In the usual case, a shallow layer of knowledge about an object or event, for example, is accessed by a person during language interpretation, as has been shown in psycholinguistic research studies of human language interpretation. This shallow layer of knowledge is the knowledge that is most likely to be true most of the time or is believed to be most likely. The term "naive" of naive semantics indicates that the knowledge required to understand language is not scientific and may not be true. The naive semantic lexicon is not intended to incorporate all knowledge or all of science. Rather, the naive semantic lexicon is intended to incorporate the same shallow level of knowledge used by a person to interpret language. By making the knowledge probabilistic, the present invention does not have to consider possible interpretations that are implausible (or false in the typical case).
The naive semantic lexicon has two aspects: ontology and description. In the ontology, a concept is classified within a classification scheme. The descriptive aspect of the naive semantic lexicon identifies properties (e.g., shape, size, function) of a concept (or phrase).
The present invention uses a classification scheme referred to as a "naive semantic ontology". The naive semantic ontology is described in Dahlgren, Naive Semantics for Natural Language Understanding, (Kluwer Academic Publishers 1988) and is incorporated herein by reference. The naive semantic ontology provides a representation for basic concepts and interrelations. Using the ontology, objects and events are sorted into major categories. The ontology reflects a common sense view (naive view) of the structure of the actual world. It encodes the major category cuts of the environment recognized by the natural language that it models, and is based upon scientific findings in cognitive psychology.
FIG. 7 illustrates an ontology that can be used to classify nouns in the English language. Entity 702 includes the Abstract 704 and Real 706 sub-classifications. Thus, an entity can be either abstract or real, for example. If the entity is classified under Real 706, the entity can be further classified as Social 708, Natural 710, Physical 712, Temporal 714, or Sentient 716, for example.
An entity that is classified as Real 706 and Physical 712, is further classified as either Living 718, Nonliving 720, Stationary 722, or Nonstationary 724, for example. To further illustrate, an event is classified as an Entity 702, Temporal 714, Relational 726, and Event 728. Event 728 has sub-classifications Goal 730, Nongoal 732, Activity 734, Achievement 736, and Accomplishment 738, for example, that can be used to further classify an event.
Using the ontological classification illustrated by FIG. 7, an entity can also be classified as Abstract 704. Within the Abstract 704 sub-classification, there is a Quantity 742, Ideal 744, Economic 746, Irreal 748, and Propositional 750 sub-classification. An entity that is classified as Abstract 740 and as a Quantity 742 can be further classified as either Numerical 752, Measure 754, or Arithmetical 756. Similarly, an abstract entity can be classified as Economic 746 and then within either a Credit 760, Debit 762, Transfer 764, or Holding 766 sub-classification, for example.
Other ontological classifications can also be used to further identify classifications for a knowledge base. Multiple ontological classifications can be used to further emulate human knowledge and reasoning. A human may cross-classify a natural language concept. Thus, for example, an entity may be classified as either Social 708 or Natural 710 using the onotology illustrated in FIG. 7. Further, the same entity may be classified as either Physical 712, Sentient 716, or Temporal 714. A Social 708, Sentient 716 is a "secretary"; a Social 708, Physical 712 is a "wrench"; and a Social 708, Temporal 714 is a "party". On the other hand a Natural 710, Sentient 716 is a "person"; a Natural 710, Physical 712 is a "rock"; and a Natural 710, Temporal 714 is an "earthquake". The ontology assumes instantiations may be multiply attached to classifications within an ontology. Thus, for example a person may be classified as Physical 712 and Living 718 as well as Sentient 716.
The lexicon 512 relates words in a natural language to the basic ontological concepts. The lexicon connects syntactic information (e.g., noun or verb) with the meaning of a word sense. The lexicon further specifies additional word-specific common sense knowledge. The lexicon specifically relates the syntactic context of each word to the possible meanings that the word can have in each context.
FIG. 8 illustrates a dictionary entry used in the present invention. As indicated in the header portion 802 of the dictionary entry, the entry contains information about the word "train". The word "train" can be, for example, a noun or a verb. In the sense portion 804 of the dictionary entry, there are seven different senses of the word train (numbered one to seven). Each entry in the sense portion 804 ("1 "-"7") identifies a common sense use for the word "train". Further, each sense identifies the ontological attachment. Each sense has syntactic information and semantic properties associated with it.
For example, entry "4" in sense portion 804 identifies a word sense that means "instruct" such as to "train a dog to heel". The "smgac" identifier in entry "4" links this meaning of "train" to a node in the ontological scheme. Sense portion 804 contains other senses of the word "train" including "set of railroad cars pulled by engine" (noun, entry "1"), "line of people, animals, vehicles" (noun, entry "2"), "part of long dress, eg for wedding" (noun, entry "3"), and "she trained as a singer" (verb, entry "5").
An entry further includes syntactic information. Entry "4" has syntactic properties as indicated in syntactic portions 806 and 808. Syntactic portion 806 indicates that the sense of the word "train" identified by entry "4" in sense portion 804 is a verb that takes an accusative object with a prepositional phrase beginning with the word "in", for example. Other syntactic features are identified in syntactic portion 806. For example, sense entry "4" of sense portion 804 can be a verb with an accusative object and an infinitival (e.g., train someone to. . . ).
A dictionary entry further includes semantic features. The semantic features portion 810 can, for example, provide coherence information that can be used to form relationships such as those formed in the coherence module 510. For example, in entry 812A, the consequence of the training event is identified. That is, for example, the consequence of the training event is that the entity trained has a skill. Further, as indicated in entry 812B, the goal of being trained is to have a skill. As indicated in entry 812C, knowledge is what enables one to train.
To understand and interpret input, people use any number of different concepts. People are not limited to a finite number of primitive concepts from which all other concepts are generated. Therefore, lexicon 512 includes entries that represent natural language concepts that can themselves be represented in terms of the other concepts, in much the same way as people formulate concepts. In the present invention, concepts do not have to be represented in a language consisting only of primitives. In the present invention, an open-ended number of different concepts can occur as feature values.
The semantic features portion 810 contains semantic feature values (e.g., entries 812A-812C) that represent natural language concepts. Preferably, these feature values are expressed in FOL for ease of deductive reasoning. Each semantic feature value can contain elements that are part of the representation language and also elements which are other concepts of natural language.
Referring to FIG. 8, "cons-- of-- event" in entry 812A and "goal" in 812B are examples of elements of a representation language. The feature values "knowledge" (in feature entry 812C) and "skill" (in feature entry 812B) are not elements of the representation language. Rather, they are themselves natural language concepts. The "knowledge" and "skill" concepts each have a separate entry in the dictionary (or lexicon).
Preferably, the dictionary information is represented in data structures for access during processing. For example, the basic onotological information is encoded in simple arrays for fast access. Further, propositional commonsense knowledge is represented in first order logic (an extension of "Horn clause" logic) for fast deductive methods.
The tasks performed by modules 502, 504, 506, 508, 510, and 512 can be performed serially or in parallel. In the preferred embodiment, the tasks are performed in parallel. Using parallel processing, the interpretation processes performed by these modules can be performed faster.
The naive semantic lexicon includes at least the following properties: 1) psychologically-motivated representations of human concepts (see senses 804, syntax 806, and semantic features 810); 2) shallow, superficial common sense knowledge (see semantic features 810); 3) knowledge is open-ended and can contain other concepts as well as elements of a representation language (see semantic features 810); 4) concepts are tied to natural language word senses (see senses 804); 5) concepts are tied to syntactic properties of word senses (see syntax 806); and 6) feature values are expressed in FOL for ease of deductive reasoning (see semantic features 810).
As previously indicated, the present invention can be used to provide a computerized system for retrieving text from a document database in response to a human language query. For example, the user describes a topic, or question, in a human language (e.g., English). The system displays a list of relevant documents by title in response to the query. An indication of the perceived relevancy of each title is also indicated. The user can use this indication to determine the viewing order of the returned documents. The user selects a document to browse by selecting its title. Once the user selects a document, the document is displayed in a scrollable window with the relevant sections highlighted.
A document retrieval system such as the one just described comprises two distinct tasks: digestion and search. Both tasks use the natural language interpretation capabilities of the present invention. FIG. 2 provides an overview of a text retrieval system that uses the natural language interpretation capabilities of the present invention. At step 202, Textual Information is input to the Natural Language Understanding (NLU) module, and at step 206 the NLU module generates a cognitive model of the input text 202. The cognitive model 208 is in the form of FOL. Cognitive model 208 contains inferences from what the text says directly and is based upon inferences from the world knowledge.
Once a cognitive model is generated for the document database, a search request can be used to specify the search criteria. The document(s) that satisfy the search criteria are then retrieved for review by the user. Thus, at step 204, a query is input. The NLU module is used to generate a cognitive model of the search request at step 208. At decision step 210 (i.e., "Similar (I,Q)?), the cognitive model of the search request is matched to the cognitive model of each document in the database. If they are similar, the information is retrieved at step 212. If they are not, the information is not retrieved at step 214. Steps 210, 212, and 214 can be repeated while there are still documents to compare against the search request or the search is aborted, for example.
The process of digesting the information input at step 202 is performed independently of the process of understanding the search request input at step 204. The process of digesting the information can, therefore, be performed in a batch mode during non-peak time (i.e., the time that the system is normally used for text retrieval). The process of understanding the search request and retrieving the text can be performed any time that a user wishes to perform a search. By separating the resource-intensive digestion of the information from a search request and information retrieval, a timely response to a search request can be provided to the user.
The first of the two independent processes, the process of digesting information, uses the NLU module along with the input textual information to generate a cognitive model of the text. FIG. 3 illustrates a model generation architecture. Textual information 302 becomes input to the NLU module at step 306. The output of the NLU module is a cognitive model 308 in FOL. The cognitive model and the textual information are connected by the concept index 306 which locates the concepts in the cognitive model in the original text for display. Notice that disambiguated, interpreted concepts from the meaning and content of the text are remembered, rather than the word patterns (strings) which are indexed in keyword-based text retrieval.
The concept index 306 is used in the second process to retrieve text. FIG. 4 provides an illustration of a Text Retrieval Architecture. Preferably, a search takes place in two passes. The first pass, a high recall statistical retrieval, locates a "long list" of relevant documents. Instead of basing the statistical approach on the words in the text, the statistics can be based on the disambiguated concepts that appear in the text. The second pass, a relevance reasoning pass, refines the "long list" to produce a "short list" of selected texts that are truly relevant to the query.
The flow of processing begins with a query or search request that is stated in a natural language (e.g., English) at step 402. The query is input the NLU module at step 404. The NLU module generates a cognitive model of the query at step 406. At step 408, the High Recall Statistical Retrieval (HRSR) Module applies statistical methods in parallel to the concept index to produce the "long list" of relevant texts. The HRSR Module applies a loose filtering mechanism, for example, to find all of the relevant texts (and potentially some irrelevant ones). At step 410, the "long list" becomes the input to the second pass performed by the Relevance Reasoning (RR) Module that refines the "long list". At step 412, the RR selects the truly relevant texts from the "long list". The entire cognitive model of each text in the "long list" is brought into memory and compared with the cognitive model of the query. The RR module applies FOL theorem-proving and human-like reasoning. The output, at step 414, is a "short list" that identifies all of the text that is relevant to the query. At step 416, the "short list" can be used to generate windows that include, for example, the "short list" and a display of the relevant text. At step 418, one or more such windows are displayed for review by the search requester.
Thus, a Natural Language Understanding System has been provided.


text:
Citizens who wish to address the Board may do so by signing in prior to the start of the Board meeting at 5:15 p.m. Speakers are generally allotted two minutes to address the Board.
Board members do not receive a salary; however, they do receive a per diem of $25 per scheduled board meeting.
The Charleston County School Board is comprised of nine local citizens, each elected for a 4-year term. Elections are held in November of even-numbered years.

subset: commoncrawl


text:
It's difficult testing a product which promises so much and has been so regularly hyped in the press (Pippa Middleton this, waiting list that). I was sent a bottle of Lancome Visionnaire before it was officially launched to test out and I did wait a week or so before my skin had settled down before starting to incorporate it into my routine, before my moisturiser.
What does this serum do?
Honestly? I am scared of using any skincare which promises to be at the edge of technology – I wonder if my sensitive skin can take it.
I have never used Lancome's other huge Serum, Genifique – but apparently that is for anti-aging for women 35+ whereas Visionnaire can be used on women of all ages and skin types – because pores and un-evenness affects young women too, right?
Here is the serum, which is very light weight, like a gel. It has a white/pink pearl going through it which adds a brightening touch to the skin.
My first few uses I experienced slight itching. It went away soon enough but then I experienced flaking – quite a lot of small pieces of dry skin coming off which stopped after 2 weeks.
After 12 weeks of using this serum I have both positive and negative thoughts about this product….
On one hand the product has a lovely texture. It sinks in quickly and doesn't feel like a burden on the skin. I noticed my skin looked better in terms of brightness and tone; it didn't fade out any pigmentation I had but there was a general clarity which was improved. I continued to get odd spots; I wonder if my skin reacted to this product at all because I got spots in places I don't usually.
On the negative, I don't like any strong fragrance in skincare and this is quite strong, Also I didn't see a massive difference pore wise. It helped those areas look smoother, but I didn't see a massive reduction in the actual size; so this product may be good if you want clarity and a finer texture but not so much for pore size. I guess there isn't one serum that does EVERYTHING.
There is also the initial stage where your skin gets used to it; mine tolerated the product properly after a few weeks but you may find it takes longer or no time at all! I wouldn't have continued with this had I experienced any patches of flaking, redness, pain or stinging.
At £57 for 30ml I don't think it is that badly priced for a high end serum (buy it here from Boots). I would def get a sample before buying a full size if possible just to make sure you don't react to it (if you have sensitive skin). It worked fine on my skin type which is dry – oily – sensitive – a bit of everything.
Have you tried Lancome Visionnaire?


text:
CB350 Rear Set
Old School Speed bolt rear set kit for your custom 350 Cafe is designed to address kicker clearance issues and brake linkage setups. This kit has mounts with proper spacers and peg location aiding in kicker clearance. Kit Includes: Bike Specific Mounts, Offset Levers, Folding pegs, toe pegs, a bike specific linkage kit, and all necessary mounting hardware. Instructions are also provided for a pain free installation.
Manufacturer : Old School Speed
Category :
MC Parts
Foot controls, Kickers & Pegs
Fab Parts
Brakes, Wheels & Shocks
Billet Rear Sets


text:
Come and learn to be more present, comfortable, and masterful in your body and in your life.
Trauma, stress, and postural compensations change the way the brain senses and moves us, and how we experience ourselves on a physical and emotional level. If you have "tried everything" to relieve your back, neck, shoulder, or hip pain for the long term and have only experienced short term relief (or none at all), then this course is for you! Eliminating muscle pain, stiffness, and the feeling of 'getting old" starts with the brain. Clinical Somatics teaches you to restore fluid, effortless, and joyful movement – on your own, wherever you are and whenever you need it.
This course is a comprehensive introduction to the core principles of Clinical Somatics: the science, philosophy, techniques, and Somatic Movements. It is for anyone with an interest in learning about or seeking to deepen their understanding of Clinical Somatics for their own personal education and exploration. No prior experience or training is required to participate. This course is also appropriate for those in the movement or bodywork field: fitness trainers, physical therapists, massage therapists, or anyone seeking to learn how to incorporate Somatic principles into their primary practice - or their life!
How Sensory Motor Amnesia contributes to most chronic muscle pain
How three universal stress reflexes affect our daily lives, mentally, emotionally and physically
The benefits of pandiculation vs. stretching
How to apply the movements, principles, and concepts of Hanna Somatics to your life
A basic routine of Somatic Movements that will result in renewed muscle control and freedom of movement
And more…
If you are seeking a long-term solution to pain relief, want to regain your freedom of movement, or are simply looking for answers, then this course is for you. It's never too late to learn to live your life pain-free!
Course Specifics
You will be led through Somatic Movements to explore your own movement areas of Sensory Motor Amnesia. You will learn a basic routine of Somatic Movements that can be practiced daily in order to restore muscle function and somatic awareness. We will explore the technique of pandiculation, a powerful technique that restores sensory and motor function. Participants will receive a free digital download of our Pain Relief Through Movement audio CD and all appropriate learning materials.
$375 USD early bird price
$425 USD if paid after Feb. 3, 2019
Please note: all course fees are listed in USD
This course is taught by Gena Rho-Smith, CCSE.
Mar. 2 | Saturday | 9:00am – 5:00pm
Mar. 3 | Sunday | 9:00am – 5:00pm
Please plan to arrive 15 minutes early to get settled so that class may start on time. There will be a 60-minute lunch break each day.
Water bottle
Yoga mat. Blanket or small cushion for propping optional.
Pen/pencil, notepad if desired. You will also receive a manual.
Wear comfortable clothing that does not restrict movement. No jeans, skirts, or dresses.
There are restaurants, cafes, etc., nearby, but you may bring a packed lunch if you prefer.
Essential Somatics® reserves the right to cancel any event or change event dates if necessary. In the unlikely situation that this may occur, a full refund of all monies paid will be returned to the client. For this reason, we highly recommend purchasing travel cancellation insurance to protect your trip investment in the event of date changes, illness, emergency, weather conditions, or any unforeseen circumstances


text:
Do romantics need to go to rehab?
There's rehab for drug and other addictions, so why not for love?
What about a place for you to detox from those people in your life that represent the Mr (or Ms) Big type?
I remember a time when Mr Big was just a ballad sung by a big haired bloke who wanted to be with an equally big-haired, double-denim clad lover.
Then Mr Big happened to me.
On a balmy summer night in the middle of a vineyard.
For those playing at home, when I say "Mr Big" I'm referring to Carrie Bradshaw's – on-again, off-again, chase her around the world, leave her at the altar, then return to put a ring on her finger – lover from the Sex and the City franchise.
My version however kissed me for the first time a decade ago in block D2 of the Shiraz vines of a rather non-descript winery. I can still recall the exact moment I lost my heart, my head and my olfactory senses amidst a bouquet of Passion Pop, marshmallow Lip Smacker and Davidoff's Cool Water cologne.
Even before Mr Big, both the '80s band and the iconic '90s TV character, momentous heart wrenching tales of love were occupying the minds of generations who grew up in a time of black and white television, however matters of the heart weren't so ... black and white.
I heard a story about a young woman in the late '50s who was smitten with a man who took her dancing, brought her flowers and supported her on her quest to become a career woman.
Everything was coasting along nicely, life was peachy and every weekend they swayed along together to Doris Day's By the Light of the Silvery Moon, until an unexpected and uncomfortable bump appeared along their road to happiness.
He was ashamed while she hid their growing product of love from the world under tight corsets and a busy social life.
Fast forward nine months and this woman was lying in a hospital bed cradling a newborn surrounded by a surprised yet supportive family but the man, his charm and his love were long gone.
Nobody gets out of love alive
Today, even after everything she went through more than half a century ago, her face still lights up and her eyes sparkle whenever his name is whispered.
She went on to marry another, yet the only vinyl record she cannot bring herself to throw away is that EP by Ms Day.
This isn't a scene I found on the cutting room floor of a Mad Men editing suite, it was my grandmother's very own version of Mr Big.
While the Sex and the City and other pop cultural references may induce eye rolling or even cause you to murmur "I have a Mr/Ms Big in my life" too, it turns out there is now such a thing as a scientifically proven 'Mr Big Syndrome'.
While we may think he (or she) is that one great true love, that love represents a form of addiction that most of us at some point in our lives may have, and have to learn how to get over.
Anthropologist and relationship scientist Helen Fisher who conducted a recent study, titled Reward, Addiction and Emotion Regulation Systems Associated With Rejection In Love, drew the hypothesis that romantic love is like an addiction.
"My guess is that our modern addictions - nicotine, drugs, sex, gambling - are simply hijacking this ancient brain pathway that evolved millions of years ago, that evolved for romantic love. The brain system evolved to focus your energy on an individual and start the mating process," she wrote in the Journal of Neurophysiology.
Along with her team of researchers at New Jersey's Rutgers University, Dr Fisher studied the brains of heartbroken participants and found that rejection by a great love ignites areas of grey matter in ways that are normally associated with addiction and craving.
"Nobody gets out of love alive," she added.
"You turn into a menace or a pest when you've been rejected. That's when people stalk or commit suicide. There's a very powerful brain system that has a dramatic effect on your entire life.
"Our poets, our songs, our novels, our sitcoms, our operas, our plays, have been discussing it forever and now we can confirm it with what we found in the brain."
Her advice for dealing with a soul shattering break-up or he (or she)-is-just-not-that-into you scenario?
"It really is an addiction; you have to treat it as an addiction," she said.
Cold turkey is her preferred scientific method - meaning enduring friendships, no emails, no phone calls, no SMS and Facebook stalking should be kept to a minimum.
"It's like trying to give up cigarettes and having one every afternoon. It's just not going to happen."
Robin Norwood, in her book Women Who Love Too Much, said love and addiction go hand in hand for some people.
"You may be predisposed, emotionally and biochemically to becoming addicted to drugs, alcohol and/or certain foods (particularly sugary ones)," she pointed out on her "Characteristics of Men and Women Who Love Too Much" check list.
So serious is love addiction there is also a movement called "LAA" - Love Addicts Anonymous. The group has been operating since 2004 and has a burgeoning online, supposedly non-denominational community of members who follow a 12-step program.
"We admitted we were powerless over love, romance, fantasies and relationships – that our lives had become unmanageable," Step One attests.
The program also makes 12 promises. It is these promises which the love sick can invest in according to the program as at the end recovered addicts will be equipped with the tools necessary "to be able to tell differences between fantasies and reality".
Let me know what you think about love addiction by joining in the conversation on my Facebook site My Jennaration.
Jenna Clarke is fashion and lifestyle writer/editor with Fairfax Media.


text:
Famous "Twilight" series actress Kristen Stewart fears for her life because of social media sites like Facebook and Twitter, according to reports.
Stewart, 22, says that social media sites allow people to find out where she is at all times, "and she's afraid that information will fall into the hands of the wrong people," the San Jose Mercury News reported.
"Nowadays it's harder because everyone is on Facebook and everyone knows where you are all the time, and everyone's twittering," said Stewart, according to author Talia Soghomonion's e-book entitled, "Robert Pattinson, Kristen Stewart, Taylor Lautner -- In Their Own Words."
Stewart continued: "Like I'm going to die because somebody is going to say where I am and somebody is going to kill me.
"Someone's going to Twitter my location and then it's going to be like, boom."
The starlet's first brush with the celebrity machine was on the day the first "Twilight" movie was released, when she was photographed sitting on her porch with her dog, smoking a pipe, the newspaper reported.


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Many people work hard and spend their health trying to achieve wealth.
Then they retire and spend their wealth trying to get back their health.
If you are like so many people today, you're either short of money and working at something you don't like in order to get more, or you have more than enough money and not enough time to enjoy the good things in life.
I was in that position once – over 20 years ago. Then something changed and I've been doing what I love and enjoying the good life ever since.
And now it's time for me to not only live the good life, but to share with people like you the amazing journey that brought me to this place of contentment and deep fulfilment.
In Search of Simplicity is my true, exciting and serendipitous journey through the wilds ofPapua New Guinea, theHimalayas, around the planet and into the heart of life guaranteed to change the way you see the world.
In Search of Simplicity offers a gateway into a world in which your deepest dreams and wishes are fulfilled, even before you are aware of them, a gateway into that place of simplicity where you stop struggling and trying to make it happen and simply allow it to happen.
When you're in the right place, doing just what you want to do, and genuinely enjoying it, the Universe bends over backwards to ensure your success.
The small investment required to buy this book could just transform your life, as the events described in page-turning style transformed mine. At the very least you'll enjoy a tantalizing read.
Click here to read just a few of the endorsements of satisfied readers from all walks of life.
In Search of Simplicityis a startlingly poignant and inspiring real-life endorsement of the power of thought, belief and synchronicity in one's life. It has been compared with James Redfield's The Celestine Prophecy and Dan Millman's Way of the Peaceful Warrior. It is a page-turning adventure story and it's TRUE.
Listen to a three minute audio trailer here:
In Search of Simplicity offers a gateway into a world in which your deepest dreams and wishes are fulfilled, even before you are aware of them, a gateway into that place of simplicity where you stop struggling and trying to make it happen and simply allow it to happen.
When you're in the right place, doing just what you want to do, and genuinely enjoying it, the Universe bends over backwards to ensure your success.
'This book has important keys to living well. Far from predictable, it is an exciting read; showing us that living with simplicity can lead to stimulating adventures, fresh insights and a deeper understanding of life.'
Suzanne Stewart, Wellington
In Search of Simplicity
will soon be available in eBook format. More details to follow.
From the Author:
My search for simplicity carried me all over the world. You could call it a search for a simpler way of living, a more natural way of being.
The external journey culminated in living for periods of time with two sets of agrarian people, one Islamic, the other Christian—the Hunzas in their Shangri La stronghold at the roof top of the world and the Trobriand Islanders at their doorstep perched on coral islets in the western Pacific. Each of these societies lives with grace and in balanced harmony with nature.
The internal journey took place while staying in Dharmsala, home of the displaced Tibetans and their leader, the Dalai Lama, in northern India. That journey carried me home to the place we're all seeking, to the source of sustenance spoken of by every mystic, to the Heart of Life itself.
It is a story of import to every human being because it is the journey we are all on, a search for the meaning of life.
The story is filled with coincidences to the point of disbelief. The journey brought me in contact with inspiring people who dared to be different, who have dared to be themselves.
It is an epic adventure. It is a story of love. It has helped me to live my life with a simpler perspective and more meaning. I trust it does the same for you.
Years after the events described in In Search of Simplicity took place, I read The Celestine Prophecy for the first time. It feels like my personal journey is like that of the reluctant fictional hero created by James Redfield. I continue to be swept along on a great adventure (of my own making) to discover and to share the magic and the mystery of life. It's waiting for us all behind every smile and with every breath of the wind.
IN SEARCH OF SIMPLICITY by John P Haines …a truly amazing true story travel/adventure book by a loving and thoughtful man.
Amanda McBroom said this February 25, 2009 on www.amcbroom.com
Amanda is an outstanding actress, singer/songwriter and is the composer of the 1979 worldwide hit, The Rose.
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Is it time to start talking about a depression?
We now know that the UK economy contracted by 0.3% at the end of last year. This followed Olympics-flattered figures in the previous three months, when there was a bit of growth.
It continues the pattern of recent years, and every time there are two quarters of contraction in a row, we get excited about a recession, and then when there is growth again we get excited about the recovery.
Yet the UK economy is still considerably smaller than it was when the crisis started in 2008.
The influential National Institute for Economic and Social Research says we have been in a depression since then, and will not emerge until the economy reaches its 2008 level.
'Sideways slide'
And yet the word depression has rarely been mentioned and some economists have gone to great lengths to avoid using it.
"I've been trying to use a different word," says Randall Kroszener, professor of economics at University of Chicago, who used to sit on the committee that sets US interest rates.
"I came up with the term 'sideways slide', which characterises where lots of economies are - not boom and bust, just sliding along, surviving."
Prof Kroszener says the key difference between a long recession and a depression is whether there are falling prices, or deflation.
The trouble is, while the definition of a recession as two consecutive quarters of negative growth is widely accepted, there is no popular definition of a depression, which means there is nowhere to go once recessions stop being relevant.
"The economy is either in high growth or in low growth, and we're on low growth," says Andrew Scott, professor of economics at London Business School.
"We're on a low trend so who cares about whether we're in recession or not?"
'Less pessimism'
David Sproul, chief executive of Deloitte UK, says he is seeing the mood improving among his client businesses.
"We're seeing much less pessimism around, but not yet optimism," he says.
"We're in an environment of uncertainty, which is very sentiment driven."
But he adds that many businesses are still concentrating on cost control to grow profits, which suggests a lack of confidence.
So although some may be talking about the possibility of the economy going into a triple-dip recession, that will ignore the fact that the economy has been basically flat for about two-and-a-half years.
The answer may not be to call it a depression, but something is needed to get the narrative beyond the obsession with tiny bits of growth or contraction every three months.
The experts quoted in this article were all attending the World Economic Forum in Davos, Switzerland.


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The New York Review of Books: 2011 May 18
Outside of a dog, a book is a man's best friend. Inside of a dog, it's too dark to read.
—Groucho Marx
All across the United States, large and small cities are closing public libraries or curtailing their hours of operations. Detroit, I read a few days ago, may close all of its branches and Denver half of its own: decisions that will undoubtedly put hundreds of its employees out of work. When you count the families all over this country who don't have computers or can't afford Internet connections and rely on the ones in libraries to look for jobs, the consequences will be even more dire. People everywhere are unhappy about these closings, and so are mayors making the hard decisions. But with roads and streets left in disrepair, teachers, policemen and firemen being laid off, and politicians in both parties pledging never to raise taxes, no matter what happens to our quality of life, the outlook is bleak."The greatest nation on earth," as we still call ourselves, no longer has the political will to arrest its visible and precipitous decline and save the institutions on which the workings of our democracy depend.


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NAIA 2014 Women's Outdoor Track & Field Athletes of the Week - No. 2
By Chad Waller, Director of Communications and Sports Information
KANSAS CITY, Mo. – Emma Buchholz of Concordia (Ore.) has been named the NAIA National Women's Outdoor Field Athlete of the Week and Ellie Staker of Embry-Riddle (Fla.) has been selected as the NAIA National Women's Outdoor Track Athlete of the Week, the national office announced Wednesday. Buchholz and Staker were selected based on their performances from March 17 – 23. This is the second award of the 2014 outdoor season.
Buchholz, a junior from Sheridan, Ore., put herself on the top of the NAIA performance list in the javelin throw at the NCAA Division III Lewis & Clark (Wash.) Spring Break Open on March 22. She took medalist honors with an effort of 154-8 (47.15-meters) and is now a full 11-feet farther than the next NAIA competitor on the season chart.
Staker (pictured), a senior from Seattle, Wash., finished first overall in both the 800-meters and 1,500-meters at the NCAA Division I Florida State Relays from March 21-22. The three-time 2014 NAIA Indoor All-American also posted two school records in the process and established championship qualifying standards. She finished in a time of 2:07.38 in the 800 and 4:29.52 in the 1,500.
For the complete Athlete of the Week calendar, click here.
Track Nominees: Aliyah Reams, Missouri Baptist; Beth Sexton, Tennessee Wesleyan; Kadie Booth, Eastern Oregon; Amelia Maguin, Southern Oregon; Jasmine Hurd, College of Idaho; Michelle Fletcher, Northwest Christian (Ore.); Daryll Rodriguez, St. Francis (Ill.); Elaine Schmeltz, Taylor (Ind.); Alyssa Foss, Indiana Wesleyan; Janna Langrell, Hope International (Calif.); Briana Zumwalt, Central Methodist (Mo.); Kimberly Garrett, Sterling (Kan.); Angie Vailas, Friends (Kan.); Cynthia Kinnamont, Berea (Ky.); Morgan Stanley, Indiana East; Darcy Mascotti, Cumberlands (Ky.); Halle Richards, Shawnee State (Ohio); Kelsey Carbajo, Grand View (Iowa); Megan Lehmkuhl, Clarke (Iowa); Alexis Browner, Wayland Baptist (Texas); Olamide Sokunbi, Brenau (Ga.); Tisa Smith, William Carey (Miss.); Olivia Bush, Southern Wesleyan (S.C.); Gregria Higgs , Florida Memorial; Joyce Miller, USC Beaufort (S.C.); Nicolette Marsh, Northwood (Fla.).
Field Nominees: Tali Fagen, Eastern Oregon; Deborah O'Dell, Warner Pacific (Ore.); Jabria Stainback, St. Francis (Ill.); Naomi Grablowski, Taylor (Ind.); Christy Schultz, Indiana Wesleyan; Katelyn Wolken, Baker (Kan.); Jessica Jones, Central Methodist (Mo.); Xannie Quiring, Sterling (Kan.); Miranda Burton, Bethany (Kan.); Brooke Doerksen, Friends (Kan.); Breanna Sims, Berea (Ky.); Alexis Brasier, Indiana East; Chanel Roberts, Lindsey Wilson (Ky.); Sam Profitt, Cumberlands (Ky.); Val Veiock, Grand View (Iowa); Tasha Pratt, William Carey (Miss.); Kehtura Merrick, Northwood (Fla.); Kiounnie Blanchard, Florida Memorial.
2014 Women's Outdoor Track & Field Athletes of the Week (Field, Track)
Week 1 (March 19): Jessica Mattos, Concordia (Ore.); Lexi Mitzel, Corban (Ore.)
Week 2 (March 26): Emma Buchholz, Concordia (Ore.) , Ellie Staker, Embry-Riddle (Fla.)


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Salvator was born during Spains Golden Age. Art, politics and wealth were flourishing. So was religion. Ignatius of Loyola founded the Society of Jesus in 1540.
Salvators parents were poor. At the age of 21 he entered the Franciscans as a brother and was soon known for his asceticism, humility and simplicity.
As cook, porter and later the official beggar for the friars in Tortosa, he became well known for his charity. He healed the sick with the Sign of the Cross. When crowds of sick people began coming to the friary to see Salvator, the friars transferred him to Horta. Again the sick flocked to ask his intercession; one person estimated that two thousand people a week came to see Salvator. He told them to examine their consciences, to go to confession and to receive Holy Communion worthily. He refused to pray for those who would not receive those sacraments.
The public attention given to Salvator was relentless. The crowds would sometimes tear off pieces of his habit as relics. Two years before his death, Salvator was moved again, this time to Cagliari on the island of Sardinia. He died at Cagliari saying, "Into your hands, O Lord, I commend my spirit." He was canonized in 1938.


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This week's new batch of downloadable tracks for Rock Band all have one thing in common. Did you catch it? The post-Easter releases from bands like Anberlin, Hawk Nelson and Skillet all fall under the "Christian rock" label.
That's a fact that wasn't overtly publicized by MTV Games and Harmonix, who simply referred to the groups as "alternative rock" acts in the official announcement that went out last week. The official Rock Band 'Zine was slightly more descriptive, calling the half-dozen new songs "from artists ranging from inspirational alternative rock to positive alternative pop."
The 'Zine writers later joked "It's like the clouds parted and a ray of sunshine shone upon the office of Harmonix... and then these songs appeared, fully authored."
Given that this is Religion Week on Kotaku, it struck us as more than a little curious that MTV seemed to be so... sneaky with its announcement, one that was recognized as a full Christian line-up by fans of the bands, but got little publicity otherwise.
And we're not the only ones. "Rapture Ready!" author Daniel Radosh writes on the site Beliefnet that the makers of Rock Band are "obviously hoping to attract the interest of the Christian marketplace, so why not throw the word Christian into the announcement as one selling point among others?"
"It's almost like they're trying to sneak that fact past non-Christian audiences who might automatically dismiss Christian rock without listening to it," Radosh theorizes "or even think that having Christian rock in the game makes the entire platform less cool by association."
MTV declined Kotaku's requests to be interviewed about the newest additions to the Rock Band library, only confirming "there already have been other Christian acts that have appeared as part of the Rock Band catalog in the past."
Rock Band makers may simply be exploring the viability of appealing to the Rock Band fan with a thirst for music with a message, appealing to the more family friendly and E-rated LEGO Rock Band crowd, which exhibits stronger restrictions on the content that can be downloaded and played. But would MTV Games go as far as making their own version of Christian music game Guitar Praise? Themed track packs we can see, but maybe not a full-fledged game.
What do you think? Do you find the video game audience too easily dismissive of content aimed at the Christian or otherwise devoutly religious gamer? For the Christians in the crowd, would you prefer more downloadable content in your Rock Band that features these types of bands? Let us know in the comments.


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wg99 wrote:so saucesauer does this mean your going to give hazen more than 5 wins this season? another note williston tops dickinson 4 to 1
Not a lot more it is suprising, but just no. Prove me wrong please but I still don't think it's going to happen they may make it to a state qualifier game but I still doubt it.