text:
Relational text index creation and searching
In an environment where it is desire to perform information extraction over a large quantity of textual data, methods, tools and structures are provided for building a relational text index from the textual data and performing searches using the relational text index.
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Priority is hereby claimed under 35 U.S.C. §119(e) to the following U.S. Provisional Patent Applications: Ser. No. 60/224,594 filed on Aug. 11, 2000 and bearing the title "Method and System for Creating A Thematic Role Based Index for Information Retrieval Over Textual Data", and Ser. No. 60/224,334 filed on Aug. 11, 2000 and bearing the title "Method and System for Searching A Thematic Role Based Index for Information Retrieval Over Textual Data".BACKGROUND OF THE INVENTION
The inventions herein relate to systems and methods for desired information located within one or more text documents. More particularly, the inventions relate to systems and methods which permit rapid, resource-efficient searches of natural language documents in order to locate pertinent documents and passages based on the role(s) of the user's search term.
In order to facilitate discussion of the prior art and the inventions with precision, the terms below are defined for the reader's convenience.GLOSSARY
Information Retrieval (IR)—The task of searching for textual information that matches a user's query from a set of documents.
Information Extraction (IE)—The task of identifying very specific elements, defined by a user, in a text. Often, this is the process of answering the questions who, what, where, when, how, and why. For example, a user might be interested in extracting the names of companies that produce software and the names of those software packages. Information Extraction is distinct from Information Retrieval because 1) IE looks for specific information within a document rather than returning an entire document, and 2) an IE system is preprogrammed for these specifications while an IR system must be general enough to respond to any user query.
Relevance—A document is relevant if it matches the user's query.
Recall—A measure of performance. Given the total number of documents relevant to a user's query, recall is the percentage of that number that the system returned as relevant. For example, if there are 500 documents that match a user's query, but the IR system only returns 50 relevant documents, then the system has demonstrated 10% recall.
Precision—A measure of performance. Given the total number of documents truly relevant to a user's query, precision is the percentage of the returned documents that were truly relevant. For example, if the IR system returned 50 documents, but only 25 of them matched the query, the system has demonstrated 50% precision.
Syntactic Roles—The subject, direct object, and indirect object of a clause. Although not strictly a syntactic role, we also include the type of verb phrase (active-voice, passive-voiced, middle-voiced, infinitive) in this group.
Conceptual Roles—Conceptual roles are a way of identifying the particular players within an action or event without regard to the syntax of the clause in which the action or event occurs. Consider the following two sentences.
1. The boy purchased an ice cream cone.
2. An ice cream cone was purchased by the boy.
In the first sentence, the subject is the purchaser and the direct object is the item that was purchased. In the second sentence, however, the subject is now the thing that was purchased and the purchaser is the object of the prepositional phrase introduced by "by." The "purchaser" and "purchased object" represent conceptual roles because they correspond to specific participants in a purchasing event. As evidenced by these two sentences, conceptual roles can appear in different locations within a sentence's syntactic structure. The advantage of using conceptual roles for information extraction over syntactic roles is that a system can extract the participants of an event regardless of the particular syntax of the sentence.
Theta Roles—Theta roles (also called thematic roles) are similar to conceptual roles in that they correspond to the participants of events or actions. In contrast to conceptual roles, the set of theta roles as defined herein is relatively constrained to include actors (who perform actions), objects or recipients (who receive action), experiencers (actors which play a role but receive no action directly), instruments (used to perform an action), dates (when an action occurred) and locations (where an action occurred). The set of conceptual roles, however, is not constrained. Conceptual roles can be defined to be appropriate to a particular task or collection of texts. In terrorism texts, for example, we may want to define the conceptual roles of perpetrator and victim, while in corporate acquisition texts we may want to define the conceptual roles of purchaser, purchasee, and transaction amount.
Syntactic Caseframe—An extraction pattern based purely on syntactic roles, e.g. "SUBJ <active-voice:kidnap>" would extract the subject of any active-voice construction of the verb "to kidnap."
Caseframe—synonymous with syntactic caseframe.
Theta Caseframe—A caseframe based on theta roles (often called conceptual roles) rather than syntactic roles, e.g. "AGENT <verb:purchase>" or "OBJECT <verb:purchase>."
Morphological Root Form—The original form of a word once suffixes and prefixes have been removed, e.g. verb conjugations reduced to the raw verb form: "reported" and "reporting" are both forms of "report."
Associative Model—The traditional approach to recognizing meaning in text. This model recognizes that certain words in association with each other generate meaning. For example, the terms "headquarters," "smoke," "alarm" and "siren" appear to generate the concept of a headquarters building on fire even though the term "fire" does not occur. Compare this approach to the Relational Model below.
Relational Model—An approach to recognizing meaning in text that takes advantage of the relationships between words. For example, the following three phrases each generate a different meaning: "headquarters on fire," "headquarters under fire" and "fire headquarters." The key to recognizing the distinction among these phrases is to recognize the relationship between "headquarters" and "fire."
Relational Text Index (RTI)—The final output which may be generated when using the invention. This is an index of events, relationships, the participants in those events or relationships, along with which document and sentence they occurred in.
Meta-type: A way of collecting specific conceptual types into a more general type. For example, if a verb normally represents a particular action, then a meta-type can be a group of verbs that could be considered synonymous. For example, the verbs "to think," "to believe," "to understand" could be considered to be somewhat synonymous, and as verbs of cognition, they give rise to the meta-type "Cognitive-action." Meta-types do not necessarily imply a two-level classification scheme. More than one meta-type may be combined into a single, more general meta-type. The meta-type, "movement-action" contains the meta-types "transportation-action" and "physical-movement-action" in which the former includes "to fly" and "to drive" while the latter includes "to walk," "to run" and "to crawl." Meta-types, therefore, represent nodes in a hierarchy of semantically related words in which each meta-type node must have at least two children. Note that common examples of non verb-based meta-types include grouping semantically related nouns or noun phrases together to include collections of dates, times, and locations.
Morphological Root Form—The original form of a word once suffixes and prefixes have been removed, e.g. verb conjugations reduced to the raw verb form: "reported" and "reporting" are both forms of "report."
POWERDRILL—A particular system that implements some of the inventions herein for information retrieval.
With the terms defined in the glossary above in mind, a discussion of the typical prior art keyword-based information retrieval systems and their weaknesses will be more meaningful.DISCUSSION OF PRIOR ART
Traditional methods for information retrieval are based on an associative model of recognizing meaning in text. Associative models identify concepts by measuring how often particular terms occur in a specific document compared to how often they occur in general. In practice, this typically means means that such systems record the content of a document by recognizing which words appear within the document along with their frequency. Essentially, a standard information retrieval system will count how often each English word occurs in a particular document. This information is then saved in a matrix, or table, indexed by the word and document name. Such a table is depicted in FIG. 1 for the search term "Now is the time for all good men to come to the air of their country."
In a typical keyword-based information retrieval system, the table of FIG. 1 would contain a column for each document in the searchable database, and a row for every English word. Since the number of English words can be enormous, many information retrieval systems reduce the number of distinct words they recognize by removing common prefixes and suffixes from words. For example, the words "engine, "engineer," reengineer" and "engineering" may be stemmed as instances of "engine" to save space. In addition, many information retrieval systems ignore commonly occurring words like "the" "an" "is" and "of." Because these words appear so often in English, they are assumed to carry little distinguishing value for the IR task, and eliminating them from the index reduces the size of that index. Such words are referred to as stop words.
When an IR user enters a query, the system looks up each query word in the table and records which documents contained the query word. Normally, each document is assigned a statistical measure of relevance, based on the frequency of the query word occurrence, which assists the system in ranking the returned documents. For example, if Document X contained a particular search term 10 times, and Document Y contained the same term 100 times, Document Y would be considered more relevant to the search query than Document X. In practice, IR systems can implement very complex statistical models that take into account more than one search term, the length of each document, the relative frequency of words in general text, and other features in order to return more precise measures of relevance to the user.
Keyword-based information retrieval is often imprecise because its underlying assumption is often invalid—that a document's content is represented by the frequency of word occurrences within the document. Two of the main problems with this assumption are that 1) words can have multiple meanings (polysemy), and 2) words in isolation often do not capture much meaning.
To illustrate polysemy, consider the word "stock." In Wall Street Journal texts, this word is most often used as a noun, meaning a share of ownership in a company. In texts about ranching, however, the word refers to a collection of cattle. In texts about retail business, the word can be a verb, referring to the act of replenishing a shelf with goods. By searching on words alone, without regard to their meaning, a keyword-based IR system returns irrelevant documents to the user. Researchers refer to this type of inaccuracy as a lack of precision.
To illustrate the issue behind working with words in isolation, consider the following two sentences.
1. The elephant ran past me.
2. The elephant ran over me.
Note that the only difference between the two sentences is the change in the preposition from past to over. Clearly, however, the sentences connote two very different occurrences. Keyword-based IR systems are unable to recognize the distinction because they do not interpret the function of the prepositional phrases "past me" and "over me" (they modify the elephant's running). Additionally, prepositions are considered to be stop words by most IR systems, so sentence 1 and sentence 2 will be represented in the keyword index as if they were identical. This type of inaccuracy is another example of a lack of precision—the user will receive irrelevant documents in response to his/her query.
Another issue with keyword-based information retrieval is that a user must be sure to enter the appropriate keyword in his/her query, or the IR system may miss relevant documents. For example, a user searching for the word "airplane" may find that searching on the term "plane" or "Boeing 727" will retrieve documents that would not be found by using the term "airplane" alone. Although some IR systems now use thesauri to automatically expand a search by adding synonymous terms, it is unlikely that a thesaurus can provide all possible synonymous terms. This kind of inaccuracy is referred to as a lack of recall because the system has failed to recall (or find) all documents relevant to a query.
Thus, in the prior art there is a clear need for a rapid and efficient search mechanism that will permit searching of natural language documents using an approach that recognizes meaning based on the relationships that words play with each other.SUMMARY OF INVENTIONS
It is an object of some embodiments of the invention to provide a computational mechanism for creating search tool that supports a model of information retrieval with greater recall and precision capabilities than a keyword model. Further objects, features and advantages of the invention will become apparent to the reader upon review of this specification, the appended claims, and the associated drawings.BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 depicts a sample information retrieval index created by a prior art keyword-based information retrieval system.
FIG. 2a depicts a structural representation of a parsed sentence.
FIG. 2b depicts a graphical view of a sentence parse and thematic role assignment according to the invention.
FIG. 3 depicts a high level flowchart of one embodiment of index creation in the invention.
FIG. 4 depicts a low level flowchart of one embodiment of index creation in the invention.
FIG. 5 depicts a flowchart indicating overall processing flow for index creation in one embodiment of the invention.
FIG. 6 depicts a flowchart indicating search processing in one embodiment of the invention.
FIG. 7 depicts overall flow of search processing in one embodiment of the invention.
FIGS. 8-13 depict screen shots for use of a search tool in one embodiment of the invention.DETAILED DESCRIPTION
The inventions disclosed herein utilize a method for performing information retrieval that is different and distinct from existing keyword-based methods. The inventions use algorithms, methods, techniques and tools designed for information extraction to create and search indexes that represent a significantly greater depth of natural language understanding than was applied in prior art search products.
There are four (4) important processes performed in some embodiments of the inventions: (a) parsing, (b) caseframe application, (c) theta role assignment and (d) unification. Parsing involves diagramming natural language sentences, in the same way that grade school students learn to do. Caseframe application involves applying structures called caseframes that perform the task of information extraction, i.e. they identify specific elements of a sentence that are of particular interest to a user. Theta role assignment translates the raw caseframe-extracted elements to specific thematic or conceptual roles. Unification collects related theta role assignments together to present a single, more complete representation of an event or relationship. The four processes are explained below.Parsing
Parsing allows a computer to diagram text, identifying its grammatical parts and the roles of words within sentences. When parsing has been completed, each sentence in the. document has been structured as a series of: Noun phrases (NPs), Verb phrases (VPs), Prepositional phrases (PPs), Adverbial phrases (ADVPs), Adjectival phrases (ADJPs), and Clauses.
As an example, consider the sentence "I bought a new printer from the office supply store." A parser might produce the following output:
I [pronoun, singular]
bought [verb]
a [article]
new [adjective]
printer [noun]
from (preposition)
the (determiner)
office (adjective)
supply (adjective)
store (noun)
This output shows the parts-of-speech for each word in the sentence, the phrase structure that encompasses the words, the voice of the verb (active vs. passive) and the syntactic role assignments of subject and direct object.
A wide range of parsers exist, with varying degrees of complexity and output information. Some parsers, for example, may not assign subject and direct object syntactic roles. Others may perform deeper syntactic analysis. For the purposes of the invention described in this document, the sentence parse above illustrates an appropriate level of detail required for proper functioning.Caseframe Application
The next step is to review the grammatical structure of the sentence and apply caseframes. Caseframes are syntactic structures that recognize a local area of context. An example of a typical caseframe might be the following:
"<subj> active-voice:purchase"
Caseframes are based on the occurrence of two elements—a trigger term and a syntactic pattern. In this particular caseframe, the trigger term is any active-voice conjugation of the verb "purchase" and its syntactic pattern is the subject of this verb (recall that the subject of an active voice verb performs the action, e.g. "John hit the ball," while the subject of a passive voice verb receives the action, e.g. "The ball was hit by John."). During processing, whenever the trigger term is found in a sentence, the system identifies the element indicated by the syntactic pattern and extracts it. In this case, the caseframe would extract the subject of any clause in which the verb phrase was a conjugated form of "to purchase." This caseframe will match any of the following phrases:
The boy purchased an ice cream cone.
Microsoft will purchase the startup company . . .
If the Mergers & Acquisitions Team would have purchased . . .
Intuitively, this caseframe gives a system the ability to identify the purchaser in a purchasing event.
Caseframes must either be hand-crafted or built with automated tool from a set of sample texts. Hand-crafting caseframes can be a tedious and time-consuming process, but it leads to a set of caseframes that are very specific for a given task. To create caseframes automatically, a system must start with raw caseframe patterns and then exhaustively create all possible caseframes that can be derived from those caseframe patterns. For example, the caseframe pattern "<subj> active-voice" would give rise to the caseframe "<subj> active-voice:purchase" when a sentence containing "to purchase" in the active voice was processed. The set of caseframe patterns is not defined by any standard.
In this invention, caseframes are created during the indexing process, i.e. as each sentence is parsed, the system generates the caseframes that are derived directly from the current sentence. In the three example sentences above, each would generate the caseframe "<subj> active-voice: purchase."Theta Role Assignment
Once a sentence has been parsed, and caseframes have identified elements to be extracted, theta roles are assigned to those elements. Theta roles can be applied in two ways. Generic theta roles includes actions (what people and things do), actors (people and things that perform actions), objects (recipients of those actions), experiencers (people and things that participate in an action but neither perform nor directly receive the action), and specifiers (modifications that restrict the interpretation of an action or participant). Conceptual theta roles are defined according to a particular caseframe, and typically this is useful in a specific subject area. For example, where generic theta roles describe broadly applicable thematic roles, conceptual theta roles can describe the legal thematic roles of plaintiff, defendant, jurisdiction, charges, damages, etc.
Note that while generic theta role assignment requires no extra data for processing, performing subject-specific conceptual role assignment requires a file that maps syntactic caseframe extractions to specific conceptual roles based on the caseframe itself.Unification
A sentence often generates more than one theta role extraction, and the process of unification reunites those extractions into a more formal, and more complete, representation of an event or relationship. In the sentence, "Microsoft will purchase the company during Q3 of 1999 . . . ," theta role assignment may identify multiple elements:
Action: purchase
Purchaser: Microsoft
Purchasee: the company
Time: Q3
Time: 1999
Unification reconciles the structure of the parsed sentence with the thematic roles that were extracted to create a single representation of the event:
Corporate_acquisition event (purchase):
Purchaser: Microsoft
Purchasee: the company
Time: Q3 of 1999
In this example, the labeling of the combined event as a "corporate acquisition" is an optional element that makes for easier reading and some additional functionality in some embodiments of the inventions.File And Sentence Information Gathering
Part of the Relational Text Index includes reference to where an extraction occurred, both in terms of document and sentence. This part of the process records a number of document-specific data elements, including the filename, the location, the revision date, the format (e.g. Word, Postscript, ascii), security access code, and source (e.g. Wall Street Journal or General Electric website). Each sentence is recorded by its beginning byte offset and ending byte offset within the document. This information allows downline systems to retrieve an individual sentence from the document.Index Creation
The final step in the process is to produce a set of indices that correspond to the extracted elements and relationships identified during the prior steps. These indices are generated as text files that can be loaded into a database system for later querying. Collectively, the following six files represent one embodiment of the Relational Text Index:
1. File Information
This file contains a unique key value, generated during this stage of processing, the filename of the original document, the full path to file, the location of the file, the revision date, the original file format, any security access codes associated with the file, and the source of the file.
2. Sentence Information
This file contains a file key value (from FILEINFO), a sentence key value, generated during this stage of processing, and beginning and ending byte offsets.
3. Semantic Hierarchy Information
If the parsing stage used a semantic hierarchy to add semantic features to an extraction, e.g. "Microsoft" may be recognized as a company name, these semantic features will be added to the Relational Text Index via two output files—the HIERACHY file and the CATEGORY file. The HIERACHY file records a term (e.g. "Microsoft"), its parent in the semantic hierarchy (e.g. "software_company"), and a flag indicating that this semantic feature is either a verb or a noun. This file, then, gives a later system the ability to file all terms known to be software companies. The CATEGORY file records the structure of the semantic hierarchy by relating a given semantic feature (e.g. "software_company") to its parent in the hierarchy (e.g. "general_company"). This allows a later system to reconstruct the semantic hierarchy.
4. Semantic Category Information
See previous description.
5. Generic Thematic Role Information
An AAO (actor action object) file contains an exhaustive record of the actors, actions, and objects extracted from each processed document. It contains a generated key value for the record itself and for each actor, action and object. It also contains a file ID that links back to the FILEINFO file, and a sentence ID that links back to the SENTINFO file. It records the byte offsets of each actor, action, and object. These byte offsets record both the full phrase and the head noun or verb of the extraction, e.g. if "the Seattle-based Microsoft" were extracted as an actor, beginning and ending byte offsets for both "the Seattle-based Microsoft" and "Microsoft" are recorded. Finally, the file contains both the head noun or verb and their morphological root forms, e.g. "buying" will be stored as the head verb, but "buy" will be stored as its root form.
6. Specifier Thematic Role Information
This file records caseframes that represent modification to actors, actions, and objects. For example, in "President Reagan recently traveled to Japan . . . " there are three cases of modification: "President" modifies the extracted actor "Reagan," "recently" modifies the extracted action "traveled," and "to Japan" also modifies the extracted action "traveled." We refer to these modifications as specifiers, and they are recorded in the SPEC file with an AAO record ID that links back to a record in the AAO file, an AAO role ID that links to a specific actor, action, or object within the AAO record, a type that indicates if the specifier is a prepositional phrase or not, the preposition if applicable, and the byte offsets for the specifier itself. Occasionally, the parsing stage of this invention may assign a certainty value to the specifier extraction when the sentence that generates the extraction is ambiguous. This file contains that certainty value if it is produced by the parser. Finally, the morphological root form of the specifier is stored as well.
A collection of data elements which may be used for populating the indices is described in the algorithm section of this document.An Example
Consider the following sentence:
The Department of Justice sued Microsoft for antitrust violations in federal court.
Step 1 (Parsing)
Step 1 performs parsing, which creates the structural representation depicted in FIG. 2a. In this example, the parsing system has added additional information to some elements of the sentence, e.g. the fact that "Microsoft" is semantically a company. Such additional information can assist later stages of processing, particularly the thematic role assignment state.
For a graphical representation of this sentence parse, see FIG. 2b. In this figure, parsing, caseframe application, and thematic role assignment has been performed, indicating the participants in a litigation event, e.g. Microsoft is tagged as both an object (the generic conceptual role) and defendant (the subject-specific thematic role). FIG. 2b represents the processing of a sentence after Steps 1, 2, and 3.
Step 2 (Caseframe Application)
Once parsing is complete, the system applies caseframes to the parsed sentence to identify extracted elements in the sentence. The following caseframes extract the four noun phrases in the example sentence:
<subj> active_verb:sue -> The Department of Justice
<dobj> active verb:sue -> Microsoft
<pp:for> active_verb:sue -> antitrust violations
<pp:in> active_verb:sue -> federal court
Step 3 (Theta Role Assignment)
Step 3 assigns theta roles to the noun phrases extracted in Step 2. Again, theta role assignment can operate in two modes. Using the default mode, the syntactic caseframes are translated into:
Action: sue
Actor of sue: The Department of Justice
Object of sue: Microsoft
Specifier of sue: (for) antitrust violations
Specifier of sue: (in) federal court
When assigning conceptual roles, the syntactic caseframes are translated into:
Action: sue
Plaintiff of sue: The Department of Justice
Defendant of sue: Microsoft
Charges of sue: (for) antitrust violations
Jurisdiction of sue: (in) federal court
Step 4 (Unification)
At this point, each extracted theta role is considered an individual element. In Step 4, unification collects these individual elements into a single event definition:
Litigation_event (sue): (based on default theta application mode)
Actor: The Department of Justice
Object: Microsoft
Specifier: (for) antitrust violations
Specifier: (in) federal court or
Litigation event (sue): (based on optional domain-specific
theta application mode)
Plaintiff: The Department of Justice
Defendant: Microsoft
Charges: (for) antitrust violations
Jurisdiction: (in) federal court
As a consequence of performing the foregoing steps, and RTI can be created as described below.Relational Text Index Creation Algorithm
The inventions use the tools of information extraction (parsing and caseframes) to build an index for information retrieval with a number of steps. One embodiment of the steps to be performed is shown below, but a myriad of variations and alternatives are possible. The inventors assume that the input to the system is a collection of texts, called a corpus, that represents the collection of documents over which users will execute information retrieval queries. As the following steps are read and considered, the reader should make reference to FIGS. 3 and 4 for graphical relationships of the steps being performed.
1. For each document to be indexed:
a. (Steps 1 & 5) Parse each document. As each document is processed, record document-specific information including its name, its location, and its source. As each sentence is processed, record its location within the document.
b. For each sentence in the document:
i. (Step 2) Apply caseframes to identify events and the participants in those events in terms of syntactic roles.
ii. (Step 3) Convert the extracted entities to generic theta roles rather than syntactic roles. See the algorithm for generic theta role assignment.
iii. (Step 4) Unify individual extracted entities to a collective event definition.
iv. (Step 6) Append to the Relational Text Index information gathered from the sentence. Specifically for each extracted actor, action or object role, the process records: the role's raw form and morphological root form, the document and sentence number in which it occurred, and the beginning and ending byte offsets for both raw form and the full phrase extraction. For each specifier role, the process records: the role's raw form, the document and sentence number in which it occurred, the preposition if applicable, a certainty value (some prepositional phrase modification is ambiguous), a link back to what extracted role this specifier modifies, and the beginning and ending byte offsets for the specifier, the full specifier phrase, and the preposition if applicable. As these records are added to the Relational Text Index, the process creates key values for each record to maintain links between the records. For example, in the sentence "The boy recently purchased an ice cream cone." the system would record the following:
1. action (purchased, purchase, DOC_A, 17, 25, 17, 25)
2. actor (boy, boy, DOC_A, 4, 6, 0, 6)
3. object (cone, cone, DOC_A, 40, 43, 27, 43)
4. specifier (recently, recently, DOC_A, 100%, 8, 15, link to action record)
V. Return to item "b" until all sentences in the document have been processed.
c. (Step 6) Append to the Relational Text Index information gathered from the document itself.
d. Return to item "a" until all documents have been processed.
2. (Step 6) If the parser used a semantic hierarchy, output this hierarchy.
a. Scan the hierarchy, creating a record for each node containing its name and the name of its parent in the hierarchical structure.
b. Scan the parser's list of terms that fall into the semantic classes defined by the hierarchy, creating a record for each term containing its name and the name of its semantic class.
Implementation of this process results in automated creation of the RTI, which can then be used to quickly locate relevant portions of relevant documents without distracting the user with irrelevant documents.General Thematic Role Assignment Algorithm
General thematic role assignments, as described above, can be performed according to the following algorithm. This algorithm is provided by way of example and should not be considered limiting of the scope of the invention, since output of equal quality performed by another method can also be used by various embodiments of the invention.
For each verb phrase in a clause
a. If the verb is in the active voice (John threw Jack the ball in the park.):
i. Assign ACTION to the verb (throw)
ii. Assign ACTOR to the subject (John)
iii. Assign OBJECT to the direct object (the ball)
iv. Assign RECIPIENT to the indirect object (Jack)
V. Assign SPECIFIER to the prepositional phrases that modify the verb phrase (in the park)
b. If the verb is in the passive voice (The ball was thrown by John to Jack in the park.):
i. Assign ACTION to the verb (throw)
ii. Assign OBJECT to the subject (The ball)
iii. Assign ACTOR to the object of a "by" prepositional phrase (John)
iv. Assign RECIPIENT to the indirect object (Jack)
v. Assign SPECIFIER to the prepositional phrases that modify the verb phrase (in the park)
c. If the verb is in the middle voice and has no direct object (The ship sank off the coast.):
i. Assign ACTION to the verb (sink)
ii. Assign EXPERIENCER to the subject (The ship)
iii. Assign SPECIFIER to the prepositional phrases that modify the verb phrase (off the coast)
d. If the verb is in the middle voice and has a direct object: (The ship sank the submarine off the coast.)
i. Assign ACTION to the verb (sink)
ii. Assign ACTOR to the subject. (The ship)
iii. Assign EXPERIENCER to the direct object (the submarine)
iv. Assign SPECIFIER to the prepositional phrases that modify the verb phrase (off the coast)
2. For each noun phrase in a clause (the rocky U.S. coastline in California)
a. Assign SPECIFIER to the adjectives that modify the head noun (rocky)
b. Assign SPECIFIER to the nouns that modify the head noun (U.S.)
c. Assign SPECIFIER to the prepositional phrases that modify the noun phrase (in California)
3. For each nominalized verb pattern 1 in a clause (Rome's destruction of Athens)
a. Assign ACTION to the nominalized verb (destroy)
b. Assign ACTOR to the possessive noun (Rome)
c. Assign OBJECT to the "of" preposition phrase (Athens)
4. For each nominalized verb pattern 2 in a clause (Athen's destruction by Rome)
a. Assign ACTION to the nominalized verb (destroy)
b. Assign OBJECT to the possessive noun (Athens)
c. Assign ACTOR to the "by" preposition phrase (Rome)File Strucrures for Index Creation
Some embodiments of the inventions use unique file structures during index creation. In various implementations, files and file structures of any type desired can be used, but for the reader's interest and convenience, general information about file structures used in index creation is provided below.
Fileid (key value created by the indexing process).
Filename (name of the document, if available).
Rawfile (full path to the document, if available).
Location (location of the document, if available).
Revdate (last date of modification).
Type (document format, e.g. Word, Postscript, html, etc.).
Access codes (for security access, if available).
Source (origination of the document, e.g. "Wall Street Journal").
Fileid (link back to FILEINFO table).
Sentence number.
Begin (a byte offset).
End (a byte offset).
Term (a term, e.g. "Microsoft").
Parent (a category, e.g. "software companies").
Type (noun or verb).
Term (a category, e.g. "software companies").
Parent (a supertype category, e.g. "general companies").
AAOid (key value created by the indexing process).
ActorKey (morphological root form, e.g. "John").
ActionKey (morphological root form, e.g. "threw").
ObjectKey (morphological root form, e.g. "ball").
InfinitiveKey (morphological root form).
Fileid (link back to FILEINFO table).
Sentence number (link back to SENTINFO table).
ActorOffset (location info).
ActorLength (location info).
ActionOffset (location info).
ActionLength (location info).
InfinitiveOffset (location info).
InfinitiveLength (location info).
ObjectOffset (location info).
ObjectLength (location info).
ActorNPOffset (location info).
ActorNPLength (location info)
ActionNPOffset (location info)
ActionNPLength (location info)
ObjectNPOffset (location info)
ObjectNPLength (location info).
ActorActual (raw form of the extracted term, e.g. "John").
ActionActual (raw form of the extracted term, e.g. "throw").
ObjectActual (raw form of the extracted term, e.g. "ball").
AAOid (link back to a record in the AAO file).
Role type (a flag for preposition or non-preposition).
Certainty (a numeric value corresponding to a probability).
AAO key (link back to a the actor, action, or object in an AAO record).
Spec (morphological root form).
Prep (the preposition if available).
SpecActual (raw form).
SpecOffset (location info).
SpecLength (location info).
PrepOffset (location info).
PrepLength (location info).
Note that byte offsets can be represented either by the starting and ending offset, or the starting offset and a length—the functional difference is negligible.Overall Processing Flow For Index Creation
Referring to FIG. 5, overall processing flow of one embodiment of the inventions for RTI creation is depicted. First, documents can be collected from various sources such as websites, databases, storage media, or elsewhere. In one embodiment of the inventions, that collection process is performed by a collector program called BOWTIE, as described below. Following document collection, parsing, caseframe assignment, thematic role assignment, unification, and index creation occur to produce an RTI output. Parsing and caseframe assignment may be carried out by a program called MOAB, described below.
MOAB—This program is a parser that diagrams sentences and assigns syntactic roles to noun phrases in the parsed sentences. In addition, MOAB can operate in extraction mode. In this mode, the program takes as input a set of caseframes that it holds in memory. Given a sentence to parse, MOAB then parses the sentence and fires applicable caseframes on the sentence. Note that MOAB only indicates that an extraction has occurred by a particular caseframe. It does not record the location of the extraction. MOAB also creates caseframes from raw caseframe patterns when given a training corpus of texts. The MOAB parser is available from Attensity Corporation of Salt Lake City, Utah.
BOWTIE—This program acts as a collector for the indexing system. It performs three main tasks. First, it collects documents for indexing from various sources, e.g. web sites, hard disk directories, news feeds, database fields etc. Second, it converts documents from their original formats to simple ascii format, e.g. it converts Word, Postscript, Adobe Acrobat, etc. Third, it triggers the operation of the indexing system once its collected documents have been collected and converted. BOWTIE is available from Attensity Corporation of Salt Lake City, Utah.
Index Searching.
In the prior sections, there was discussion of document collection, parsing, caseframe assignment, thematic role assignment, unification, and creation of the Relational Text Index. Once the RTI has been created, the user may perform rapid and resource-efficient searches for documents that are relevant to his area of interest. Below, one embodiment of a way of searching the Relational Text Index is described. There are several main concepts behind this method of searching.
1. A Theta Role-Based Representation. In this model, rather than searching for the occurrence of a search term within a document's collection of words, the inventions offer the ability to search for that term when it is performing in a particular theta role. For example, a user can search for "Microsoft" only when Microsoft is the "actor," i.e. when it is performing some action. This is very different from searching for any occurrence of the word "Microsoft." (Consider "He walked across the Microsoft campus." vs. "Microsoft sued the U.S. Government." A standard keyword-based IR system would retrieve both sentences, but the theta role-based IR system would only retrieve the latter.) Currently, the invention focuses on the three theta roles of actor, action, and object. This focus is a result of the sparseness of data provided by the parser. Parsers that generate deeper conceptual representations of sentences support a wider range of theta roles.
2. Combined Theta Role Constraining. Once the user selects a search term for a theta role, e.g. the actor, action, or object roles, the system returns a list of documents in which the search term plays that particular role. In addition, the system displays a list of what other theta roles are found in the same documents in events or relationships associated with the original search term. For example, searching for "Microsoft" as an actor performs two tasks. First, it returns a list of documents in which "Microsoft" performed as an actor. Second, it returns a list of actions that Microsoft performed. The user can then narrow the query to select only those documents in which Microsoft performed some particular action, like "to sue." Thus the two theta role values have constrained the search. (The exact relationship among theta roles and how they constrain each other is defined further below).
3. Specifiers. In this model, any theta role can be specified by certain linguistic constructions. An action, for example, can be specified by adverbs or prepositional phrases, e.g. "He ran quickly." and "He walked to the store." The semantic content of a phrase can be dramatically changed by such modification, e.g. "He will cash the check." vs. "He will not cash the check." and "The software always crashes at startup." vs. "The software occasionally crashes at startup." This model allows the user to enter specifiers that restrict the retrieved documents to very precise language based on the use of adjectives, noun modifiers, adverbs, prepositional phrases, and infinitive verbs (e.g. "tried to run" and "failed to run").
4. Meta-types. In large corpora, searching on a particular actor, for instance, can yield an extremely large number of associated actions. For example, searching on "Microsoft" as an actor will produce a list of every action the company performed in the corpus. The inventions herein manage such large lists of theta-role values with meta-types. A meta-type is a way to condense multiple theta-role values into a single, more general value. Verbs of communication, for example, to speak, to say, to talk, to mention, can be rolled into a single COMMUNICATE meta-type. A meta-type can be built for any theta role, not just verb-based action roles. A meta-type can contain other meta-types as well, thus leading to a hierarchical mechanism for maintaining semantic relationships. The user of the invention has the option of either selecting a meta-type as a search term, in which case all the theta-role values contained in that meta-type are used for searching, or drilling down into the meta-type to select a particular sub-metatype or specific theta role value as a search term.
5. Collapsing on root form. The Relational Text Index includes not just the extracted thematic roles, but also their associated morphological root forms. This allows one to search for particular roles without having to enumerate the possible variations due to conjugation, singular vs. plural use, etc. For example, the action "sue" may occur as "sued" or "sueing" and the object "reporter" may occur as "reporter." This feature also allows a user to find search terms they may not initially think of using. When searching on "airlines" for example, a search tool user can expand the located thematic role extractions to find "American Airlines," "SkyWest Airlines," "Delta Airlines," etc.
Resational Text Index Searching Algorithm.
Once an RTI or another suitable index has been created, such as has been described above or by other methods, the index can be searched by a variety of techniques. One algorithm for searching such an index is described below and depicted graphically in FIG. 6. The computer program used by the applicant to perform this applicant is referred under the trademark POWERDRILL. This algorithm assumes that an RTI of the structure and content described above has been provided, but variations using other types of indices are possible as well. This particular algorithm is considered a general search algorithm which can be used when searching based on user input for particular thematic roles, i.e. actors, actions, objects, and/or their specifiers. Steps performed in the algorithm are as follow. The reader should refer to FIG. 6 while reading these steps:
1. Read in the index of theta caseframe extractions into a searchable database.
2. Begin loop.
3. Accept from the user a term(s) for the slot of ACTOR, ACTION, OBJECT, or any of their SPECIFIERS.
4. Accept from the user an indication of whether they want the search to operate in intersection mode or union mode. Also accept an indication of whether or not to collapse results around a term's morphological roots.
5. Run query.
a. If a term exists in the ACTOR slot, search the database of extracted NPs for any extracted NPs that match the ACTOR. Record the locations of these extractions in the query extraction location pool (QELP) as ACTOR results.
b. If a term exists in the ACTION slot, search the database of extracted NPs for any that were extracted by theta caseframes which match the specified ACTION. Record the locations of these extractions in the query extraction location pool (QELP) as ACTION results.
c. If a term exists in the OBJECT slot, search the database of extracted NPs for any extracted NPs that match the OBJECT. Record the locations of these extractions in the query extraction location pool (QELP) as OBJECT results.
d. If a term exists in any of the SPECIFIER slots, search the database of extracted NPs for any specifier records extracted NPs that match user input. Record the locations of these extractions in the query extraction location pool (QELP) as SPECIFIER results. More than one specifier may be entered, e.g. an adjective modifier for the actor, and a prepositional phrase modifier for the action.
6. Display search results.
a. If the system is in intersection mode, find the intersection of the ACTOR, ACTION, OBJECT, and SPECIFIER results in the QELP. (Two locations are in the same set if they document name and sentence number match.).
b. If the system is in union mode, combine the ACTOR, ACTION, OBJECT, and SPECIFIER results in the QELP.
c. Scan the locations in the QELP for unique text names, and display a list of these names to the user.
d. Allow the user to select from the text names.
i. Since each text may contain more than one extraction location, loop through the locations in the QELP that match the specified text name.
ii. Begin loop.
iii. Get the next extraction location in the selected text.
iv. Display the sentence.
v. End loop.
7. Display alternative ACTOR/ACTION/OBJECT and SPECIFIER terms.
a. Scan the locations in the QELP.
i. From each location, retrieve the verb-based theta caseframe (verb-based theta caseframes include "agent <verb>," "patient <verb>" and "agent <verb> patient") that applied to the extraction location and display the verb in the ACTION list. Check these verbs for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
ii. From each location, retrieve any TH_AGENT-based theta caseframe ("agent <verb>") that applied to the extraction location and display the extracted NPs from those theta caseframes in the ACTOR list. Check these NPs for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
iii. From each location, retrieve any TH_PATIENT-based theta caseframe ("patient <verb>") that applied to the extraction location and display the extracted NPs from those theta caseframes in the OBJECT list. Check these NPs for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
iv. From each location, retrieve any verb-pp-based theta caseframe ("<verb> pp" which captures constructions like "killed with a gun" or "said with conviction.") that applied to the extraction location and display the extracted NPs and prepositions from those theta caseframes in the ACTION specifier list. Check the NPs (not the prepositions) for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
v. From each location, retrieve any noun-pp-based theta caseframes ("<noun> pp" which captures constructions like "priests of the church" or "trial by fire") that 1) applied to the extraction location, and 2) extracted the term(s) in the ACTOR slot, and display the extracted NPs and prepositions from those theta caseframes in the ACTOR specifier list. Check the NPs (not the prepositions) for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
vi. From each location, retrieve any noun-pp-based theta caseframes that 1) applied to the extraction location, and 2) extracted the term(s) in the OBJECT slot, and display the extracted NPs and prepositions from those theta caseframes in the OBJECT specifier list. Check the NPs (not the prepositions) for membership in any predefined meta-types, and combine any appropriate terms into meta-type groupings.
8. End of loop.
The basic steps listed above can also be augmented to cover the instance when a user wants to expand an actor or object result from its morphological root form. Steps to perform this additional function are as follow.
For each term selected by the user:
1. Capture the role the term is playing, i.e. actor or object.
2. Query the Relational Text Index for any extractions in which the term occurred in the captured theta role.
3. For each of these extractions:
a. Retrieve the location of the noun phrase that generated the extracted term, i.e. the document, the sentence, and the location within that sentence.
b. Retrieve from the document the phrase and display it.
Overall Search Processing Flow.
Referring to FIG. 7, overall processing flow for performing a search on the RTI in one embodiment of the invention is depicted. The general flow includes running a search program such as POWERDRILL to get a user query, execute the user query, display search results, and display associated theta role values. Communication with the RTI is achieved through a database server.
Although the inventors perform their searches using an RTI, other search indices could be created for us with the various embodiments of the search inventions. This particular embodiment of the invention depends on an RTI, a mechanism for locating a particular sentence within a document, and a database for serving the RTI.
An end-of-sentence mechanism is used that will normally take one of two forms. In the first case, a separate program that can perform end-of-sentence recognition is called with the document name and sentence number to locate. In the second case, a simple index of the starting and ending byte-values of each sentence in a document is consulted.
Search Examples.
The following material provides the user with examples of searching an RTI in one embodiment of the inventions. These examples assume that the POWERDRILL search program implemented by the inventors is being used to perform the search, although the inventions could be implemented using other software.
Referring to FIG. 8, a POWERDRILL screen shot is provided from a POWERDRILL installation over a set of Reuters newswire articles produced during the Reagan era. In this example, the user has told the search tool to search for events in which "Reagan" was the Actor, i.e. in which Reagan did something. In addition to retrieving a list of matching documents, the search tool displays a list of actions performed by Reagan, and a list of recipients of some of those actions. The user can now select one more of these actions or objects to refine the search.
Referring to FIG. 9, there is a screen shot depicting that the user has selected "nominate" as the Action, and the search tool responds with documents in which Reagan nominated someone, and the Object column shows the nominees. The user can expand each extracted term to show its complete context—in this case, "Webster" expands to "Federal Bureau of Investigation Director William Webster." Note also, that by double-clicking on one of the results, the search tool retrieves the sentence in which the event occurred, not the document itself.
The user can also view the entire document, with the sentence highlighted, if desired. However sentence-level of granularity of results can be tremendously valuable to reducing search time, particularly with large documents.
Referring to FIG. 10, the user has selected "Reagan" as the Actor and "Mrs." as a Specifier. The search tool now only displays events in which "Mrs. Reagan" performed some action. In this case, the user continued to drill down into the case of "Mrs. Reagan" celebrating an anniversary.
One of the problems associated with search tools is that it is often difficult for a user to pose a question in such a way that the system returns expected results. The invented search tools help address this problem in two ways. First, a user of the inventions user can consult a list of semantically related terms in crafted the query. In the screen shot of FIG. 11, the search tool is suggesting terms related to "buy" for the Action slot
Second, the invention's exhaustive indexing of the document set provides a unique ability to explore the contents of the documents, and this exploration process can lead to expanded search terms. In the example of FIG. 12, the user wanted to find other terms related to "stock." By anchoring on "investors" as the Actor, and "buy," "acquire" and "purchase" as the Actions, the search tool shows everything that investors bought, acquired or purchased. The result now becomes a pick-list of suggested terms, and while the user may not have thought about entering "warrants" or "shares," he/she will benefit from a I'll-know-it-when-I-see-it process. This ability to peruse the content of the document set in an interactive way is a unique and powerful element of the inventions.
Finally, in the example of FIG. 13, the user has expanded the object term "law" and "laws" to see the full noun phrase extraction.
Data Mining And Analytics.
Analytics, often referred to as business intelligence, is the process of driving business functions from quantitative data. For example, by recognizing that a company sells fifteen times as many tubes of toothpaste in the 6 ounce size as the 8 ounce size, the company may elect to discontinue producing the larger size to save production and marketing cost on a product that brings in little value. Traditionally, such processing could only be performed over numerical data, i.e., data that could be counted, averaged or otherwise statistically manipulated.
Using a relational text index, however, we now have the ability to mine events and attributes from textual data and feed them directly into an analytics processing system because these events and attributes can be statistically manipulated. The RTI has changed the free-form of English language text into a set of specific representations of meaning. For example, a customer may call into the consumer hotline complaining that the 8 ounce size tube of toothpaste is too large to fit in a medicine cabinet. The RTI records this event as a customer complaint with the attributes "8 ounces" and "toothpaste". If a marked number of similar calls are recorded by the hotline, analysis of the RTI will show that a large number of complaints are being received about 8 ounce sizes of toothpaste, alerting the company to the problems.
The main issue here is codifying information from unstructured text. The RTI represents meaning in a precise way, leading to the ability to recognize content of the text. Analytic processing over the RTI then is another way of using that content.
Use of the RTI in analytics permits the user to locate specific events or attributes with the text collection. For example, in a customer service database, the RTI will support the question, "What are my customers complaining about?" In contrast, in a data mining approach, the RTI supports this question: "What are my customers saying?" The distinction is on the analytics side I am asking about a specific defined event. On the data mining side, I am using the RTI to find events of statistical importance.
Computing Environment.
The inventors contemplate that the inventions disclosed herein may best be implemented using various general purpose or special purpose computer systems available from many vendors. One example of such as a computer system would include an input device such as a keyboard, mouse or screen for receiving input from a user, a display device such as a screen for displaying information to a user, computer readable storage media (including hard drives, floppy disks, CD-ROM, tapes, and other storage media) for storing both text data and software and software tools used in the invention, dynamic memory into which program instructions and data may be loaded for processing, and one or more processing for performing operations described above. The computer system may be a stand-alone personal computer, a workstation, networked computers, distributed processing across numerous computing systems, or another arrangement as desired. The documents to be processed using the inventions could be located on the computer system performing the processing or at a remote location. The RTI, once created, could be stored with the documents for later use, or it could be stored in another location, depending on the desires of those implementing the system.
While the present inventions have been described and illustrated in conjunction with a number of specific embodiments, those skilled in the art will appreciate that variations and modifications may be made without departing from the principles of the inventions as herein illustrated, as described and claimed. Any of the software components and steps described herein may be performed by custom-built software, and several of them may be performed by currently available off the shelf software that will be known to persons in the natural language processing field. The present inventions may be embodied in other specific forms without departing from their spirit or essential characteristics. The described embodiments are considered in all respects to be illustrative and not restrictive. The scope of the inventions are, therefore, indicated by the appended claims, rather than by the foregoing description. All changes which come within the meaning and range of equivalence of the claims are to be embraced within their scope.
1. Computer readable storage media having a relational text index for use in searching a corpus of text documents on a computer system, the computer system including an input device, an output device, dynamic memory and a processor, the relational text index including:
- noun phrases, verb phrases, prepositional phrases, adverbial phrases, adjectival phrases, and clauses from parsed sentences in the corpus of text documents,
- caseframe extractions applied to said parsed sentences,
- thematic role extractions corresponding to of caseframe extractions, said thematic role extractions including actors, actions and objects, location information for sentences and documents corresponding to thematic role extractions;
- wherein said thematic role extractions include generic thematic roles; and
- wherein said generic thematic roles are selected from the group consisting of actors, actions, objects, experiencers and specifier.
2. Computer readable storage media as recited in claim 1 wherein said thematic roles role extractions include conceptual thematic roles.
3. Computer readable storage media as recited in claim 1 further comprising diagrammed natural language sentences.
4. Computer readable storage media as recited in claim 1 further comprising information concerning grammatical parts of each sentence.
5. Computer readable storage media as recited in claim 1 further comprising references to where original text occurred in a document.
6. Computer readable storage media as recited in claim 1 further comprising byte offsets indicating where original text occurred in a document.
7. Computer readable storage media as recited in claim 1 further comprising raw forms corresponding to caseframe extractions.
8. Computer readable storage media as recited in claim 1 further comprising morphological root forms corresponding to caseframe extractions.
9. Computer readable storage media having a relational text index for use in searching a corpus of text documents on a computer system, the computer system including an input device, an output device, dynamic memory and a processor, the relational text index including:
- noun phrases, verb phrases, prepositional phrases, adverbial phrases, adjectival phrases, and clauses from parsed sentences in the corpus of text documents,
- caseframe extractions applied to said parsed sentences,
- thematic role extractions corresponding to of caseframe extractions, said thematic role extractions including actors, actions and objects, location information for sentences,
- documents corresponding to thematic role extractions, and
- a search tool for searching a relational text index, that includes
- a tool for accepting a search query from a user, said search query including actor and action roles, and
- a tool for querying said relational text index for instances when said actor or action is recorded in its appropriate role, for each query match, said tool being capable of retrieving the noun phrase of the extracted term, and for each query match, said tool being capable of retrieving the document and phrase where the match occurred.
10. Computer readable storage media as recited in claim 1 further comprising full specifier phrases for at least some specifiers in the relational text index.
11. Computer readable storage media as recited in claim 1 further comprising caseframe triggers.
12. Computer readable storage media as recited in claim 1 further comprising predefined meta types for at least some thematic roles for verbs.
13. Computer readable storage media having a relational text index for use in searching a corpus of text documents on a computer system, the computer system including an input device, an output device, dynamic memory and a processor, the relational text index including:
- noun phrases, verb phrases, prepositional phrases, adverbial phrases, adjectival phrases and clauses from parsed sentences of the corpus of text documents,
- caseframe extractions corresponding to said parsed sentences, and
- thematic role assignments corresponding to said caseframe extraction, and
- file information for locating documents corresponding to information located in the relational text index;
- wherein at least some of said thematic role assignments are selected from the group consisting of action, action, object, and specifier.
14. Computer readable storage media as recited in claim 13 further comprising morphological root forms for actors, actions and objects.
15. Computer readable storage media as recited in claim 13 further comprising semantic hierarchy information, semantic category information, generic thematic role information, and specifier thematic role information.
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- David L. Bean, Ellen Riloff "Corpus-Based Identification of Non-Anaphoric Noun Phases." Procedings of the 37th Annual Meeting of the ACL, Morgan Kaufmann, 1999.
Type: Grant
Filed: Aug 10, 2001
Date of Patent: Apr 27, 2004
Assignee: Attensity Corporation (Salt Lake City, UT)
Inventors: Todd D. Wakefield (Park City, UT), David L. Bean (West Bountiful, UT), Jeffrey A. Lorenzen (Park City, UT)
Primary Examiner: Diane D. Mizrahi
Assistant Examiner: Yicun Wu
Attorney, Agent or Law Firms: Daniel P. McCarthy, Parsons Behle & Latimer
Application Number: 09/927,579
International Classification: G06F/1730;


text:
(wedding book page 20) mr. & mrs. 2
Recent Comments
I love the picture on the right! So sweet!
Created By
About Me
I̶ ̶c̶o̶l̶l̶e̶c̶t̶ ̶p̶e̶z̶ ̶d̶i̶s̶p̶e̶n̶s̶e̶r̶s̶ ̶a̶n̶d̶ ̶h̶a̶v̶e̶ ̶1̶5̶0̶0̶+̶
I love the mountains and being outside
I went to Africa for a whole day once
I have an awesome husband named Bill
I love to look at pictures, my own and everyone else's
I randomly open fridges - yep - everywhere I go - I don't even know why and most of the time I open it and close it and can't even tell you what was in there - maybe it is my subconscious making sure I will have food if I am stuck there for a few days - very strange - I don't get it
instgram: https://instagram.com/pezian1984/
Type of ProjectDigital Scrapbook Layout
I had a hard time finding just one kit that would work for our wedding book. I ended up making my own kit with kits that I already had and by recoloring a few things. It is hard to remember now exactly where everything came from (and most of it is no longer available) I know that I used a kit called White Wedding for all the white and silver background papers (so on every page) and for some glitter stickers. I used a Halloween kit from PhyscoZoe and On This Day kit from Paislee Press. If you want to know about a certain element let me know and I will do my best to figure out where it came from.
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subset: commoncrawl


text:
So it happened. I finally broke a bone. Only took 30 years. It was my left pinky toe. I was a klutz and was walking by a chair and nipped it on the leg. My toe bent out at 90 degrees. It was a sight. And quickly started to hurt like a bitch. Many f-bombs were dropped.
Dad took me to the ER. Em, Lily and I were at the vacation house in Rockville. It was an Adventist hospital. Hell yeah. I [heart] the Adventist hospistals.
They saw me very quickly. The receptionist had the time to comment on my tattoo. Thanks for noticing. The doc was awesome. Numbed my toe like a champ and reset the fractured bone. The pain was completely managable. Nothing like the movies.
I am seeing a othopedic specialist later today. And I might get one pin. And I hope to get the "boot" so I don't need crutches.
Regardless, I totally have taken my feet for granted. Going to the bathroom totally wears me out. And it's like 10 feet from the couch. Oh and I have mad cabin fever. Help me. Please.
Oh, and I have been pain killer free since post-dinner time last night. They gave me vicodin. But I barely need the advil. It's painful, but it's not unbearable, and for some reason I feel like I will heal faster with out the side-effect ridden pills. And my goal is to heal faster so I can put my baby to sleep and walk my dog.
UPDATE:Â I saw the Podiatrist yesterday that was recommended to me by a family friend. He hooked me up with some mad painful, in the nerve, novocaine, but it totally and amazingly numbed my toe to all hell. And he re-set the toe again. This time, rather violently yet perfectly. He was awesome. I can't recommend this doc enough. He gave me 4 options and he explained everything all the way through. He also recommended NOT taking the vicodin unless absolutely necessary. Now what doc actually recommends not taking a drug? There ya go! Good doc. And I got the spaceboot, like I had wanted and like Elle (get better soon Elle), but I still need to keep off the foot for a week with crutches. I have an appointment with Doc-Awesome in two weeks and we'll take it from there, but he's optimistic it should be healed then.


text:
Sathya Sai Baba Sent The Clouds Away In Mumbai
Sri Indulal Shah was a fortunate witness an astounding event in Mumbai (then Bombay) and the year, 1967. This time, it was in Dharmakshetra in Bombay. The month was July, the monsoon time in Bombay, and Sathya Sai Baba was paying a visit to Maharashtra State. The Bombay Samithi had arranged a public meeting of devotees in honour of Baba at Dharmakshetra.
I remember that it was a Sunday evening. In Bombay, in the month of July, a heavy downpour can be expected any time. That being a Sunday, a large crowd was expected to attend, regardless of the possibility of rain. The Samithi had taken all precautions to provide shelter to the devotees from showers. The large pandal at Dharmakshetra was packed with people to the full. The meeting was to commence at 6 p.m.
But at about 5.15 p.m., the sky was overcast and we feared that the rains would lash down any time. We were anxious to save the devotees from getting drenched in heavy rains. Some of the office-bearers felt that we should request Bhagavan Sri Sathya Baba to start the meeting a little early, say at 5.30 p.m. I felt that Baba might not accede to this as there was 'Rahu Kal' (inauspicious time) upto 6 p.m., but my colleagues insisted that there was nothing like 'Rahu Kal' for Bhagawan.
Accordingly, I went up to Bhagavan and prayed: "Baba, could we start the meeting a little early?"
"Why?" asked Baba in a tone, which, by itself, was an answer to the question. But we did not understand this then, as our mind was also clouded like the overcast sky that evening.
I pleaded: 'Bhagavan, it is likely to rain, the meeting may be disrupted, the devotees may suffer'.
In answer to this Baba smiled and that benign smile remains imprinted in my mind even to this day – as bewitching as the smile that we see in the picture of Lord Krishna.
He then held my hand and took me to the nearby window where I saw that a drizzle had already started. Those who have seen Dharmakshetra know that Baba lives in a lotus-shaped mandir on top of a hillock and that the windows from His room open out westward. So, we could see through them the entire crowd gathered below and the dark expanse of the cloudy sky above.
Standing near the window, Baba said: 'Yes, yes, it will rain, and it will rain very heavily'.
This statement put me out completely. I could see from the window, devotees holding open umbrellas in an effort to protect themselves from the impending showers. I was searching for words to pray to Baba.
Meanwhile, Bhagawan raised His hand through the window and, in a moment, we saw the drizzle stopped and clouds dispersed in the sky!
In that moment of anxiety, the full import of what Baba had done did not strike me. Baba said that the meeting should commence as scheduled and I rushed out to convey Baba's decision to my colleagues outside.
The meeting started at 6 p.m. and, by then, the sky had brightened enough to dispel all our fears about the rains. It was during the meeting that I realised what a great blunder I had made in praying to Baba to commence the proceedings a little early. During those few minutes, I had forgotten that Baba is God and that He had absolute command over 'Prakriti' (nature).
When the meeting concluded and my colleagues were saying happily that the meeting went off very well, I shed some tears of joy and rushed to see Baba and fell at His Lotus Feet.
Baba, then, smiled and said: 'You forgot'.
When the heart is full, words are few. I had no words to convey my gratitude. I was in an ecstatic thrill. That was an unforgettable moment and I cherish the thought that it was Bhagawan Himself who reminded me that I had forgotten that He is God!
This incident is a conclusive proof that Baba is Omnipotent, that He can control nature and that He is an Avatar, God come in human form to bless all mankind.
Reference: "Sixteen Spiritual Summers" by Sri Indulal Shah. Page: 16-18. Published by Sri Sathya Sai Books and Publications Trust, Prasanthi Nilayam in 1980.
Filed under: Dharmakshetra, God, Gurus, India, Mumbai, Sai Baba, Sai Baba Updates, sathya sai baba, sathya sai baba news, Spirituality | Tagged: Avatar, Bombay, Control Over Nature, Lila, Lilas, Mahimas, miracles, Monsoon, Sai Baba Miracles | Comments Off on Sathya Sai Baba Sent The Clouds Away In Mumbai


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Ahhhh, it's finally that time when drafts are becoming more and more meaningful and abundant, we've got the REL Auction draft on Thursday, and two RCL leagues drafting this weekend which still have spots open! Don't forget the grand prize in RCL of Razzball gear and a permanent imprint of your team name shaved in my head. I mean, where else can you win a fan league and brand the resident editor? I'm not really looking forward to the looks I'm going to get (doesn't help I'm 6'7 and people be lookin' anyway!), especially if a team like Your Mom wins. Ugh.
So in order to hopefully restore my honor, my second RCL team was drafted last Thursday, and I gotta say, while I'm lukewarm to my team, you RCL leaguemates were brutal. Here's how the draft went down (full draft results):
Round 1, Pick 4 – James Harden – Really glad Harden hopscotched a pick and made it to me at 4. I think he's the clear-cut #3 guy off draft boards this year, and with SG a thin position, love having him locked down as my stud. Fear the beard, RCLers! Fear it! (Wooooo! That sort of confident exuberance got me Harden.)
Round 2, Pick 21 – Ricky Rubio – I was really hoping John Wall would fall to me here, but was probably overly-optimistic I'd get my first real reach in my ranks (Wall as a first-rounder is pretty anti-establishment). So I went with the next best available in my rankings (and who I wanted either way) in Rubio. Given an extraordinarily small sample, I wrote that I bought an improved perimeter game and he's 3-5 from deep this preseason. A healthy Love will only give Rubio's dimes love. What's a dime in Euro?
Round 3, Pick 28 – Dirk Nowitzki – Unlike the sweet and sexy and "imuy caliente!" pick of Rubio, Dirk is old, German, and reliable. It's like a Mercedes vs. a…. a Fiat? Because J Lo sings for those? Eh, car metaphor didn't work. But I've got Dirk ranked at 23 on my board and wanted to go ahead and grab a big. Liking my team so far.
Round 4, Pick 45 – Tony Parker – What is this, 2002?! Yes, my last two picks were gramps and could star alongside Johnny Konxville in the sequel to whatever-the-hell he's got coming out now, but I have Parker at 28 on my board and he's coming off another fantastic season. Surprised he lasted this long.
Round 5, Pick 52 – Jonas Valanciunas – Whoaaaa, JB is putting his money where his mouth is! Yes, the Luminescent Lithuanian, picked with my lucky number in the draft (52), is going to lead me to glory. Sure, I may have wanted Jeff Green really bad here (and I have Green ranked higher by a few spots), but that doesn't dissuade me from a do-it, show-it boasting regarding my rankings.
Round 6, Pick 69 – Jose Calderon – Again, you guys in my league suck. If I had gotten Tobias Harris here (went 4 picks earlier and Yahoo having him in the 80s I believe is insane) I would've had so much man love I might play out the Fantasy season on Brokeback Mountain (R.I.P. Ledger). But getting Calderon here is no joke[r] and I look forward to some really elite PG play to carry me to the finals. Shaving my own name in my head I say, my own name!
Round 7, Pick 76 – Gerald Henderson – Going with my first true reach, I was actually relieved Greg Monroe got picked a few spots ahead of me so I wouldn't be stuck with him. Not a big Monroe guy, but this far down woulda been nice. Anywho, I love Henderson this year who played some real solid ball down the stretch last season – lost in the noise of the Bobcats diarrheaing their way to a 21-61 record. "When you're named after a stupid owner, and every year your have a lottery pick boner, diarrhea! Diarrhea!"
Round 8, Pick 93 – Andrew Bogut – Was first hoping the long slide of O.J. Mayo might make it to me in my longer layover between picks, and then my sights were on Wilson Chandler, who i think breaks out this year. Alas, twas not, and twas a night before Christmas where all I wanted was a healthy Bogut season. I needed a big, and Bogut is a stat-stuffer when healthy in everything but scoring, and I've got scoring pretty well shored up. Don't fill my stocking with coal, Bog! Wait, do they have Christmas in Australia? I guess it would be in the middle of Summer… Never thought about how that would work…
Round 9, Pick 100 – Brandon Knight – The talent pool gets pretty thinned out once you hit triple-digits, and with Knight at 84 in my ranks with me seeing a mini-breakout, this was a no-brainer. Kid has great upside and I think the change in scenery and the best guard he's been paired with in Mayo will lead to a solid season.
Round 10, Pick 117 – Kentavious Caldwell-Pope – Sorry to call ya out bro, but Hobo head lice!? Taking John Henson before I could!? For shame! First I wanted to be angry that you took him before I could, but then saw you followed it up with Robin Lopez?! Sideshow "worst player in the NBA"?! I'm just bitter Henson isn't on all my teams now. So welcome to the fold, KCP. With all the guards in the Detroit backcourt getting banged up, I moved KCP to 96 in my ranks in what will be a boom-or-bust rookie season.
Round 11, Pick 124 – Michael Kidd-Gilchrist – A guy I have edged in my top-100 as well, I knew I could wait on MKG, then sit back and enjoy multi-cat production. He's still really raw but was the youngest player in the NBA last year, and while he won't be too much of a scorer, he'll steal and block and share the rock! Dude, that's an awesome new catch phrase I'ma use this year.
Round 12, Pick 141 – Ben McLemore – Keeping it real with young upside late, McLemore has carved out a starting role and will take a lot of shots. He went 10-16 for 23 points last night. Not married to him, but upside.
Round 13, Pick 148 – Randy Foye – Of course someone would take Corey Brewer before me! My Mr. Irrelevant is just that… We'll see a game or two in, but he's my first drop if any last-minute preseason news goes down or whoever is the hot wire pickup in week 1.
|JB Gilpin – HUMAN
|Ricky Rubio
|James Harden
|Tony Parker
|Gerald Henderson
|Dirk Nowitzki
|Michael Kidd-Gilchrist
|Jonas Valanciunas
|Andrew Bogut
|Jose Calderon
|Brandon Knight
|Kantavious Caldwell-Pope
|Ben McLemore
|Randy Foye
What say you, Razzball and RCL Nation? I personally like the team, and after more introspection putting the post together, have gone from lukewarm, to just above tepid. I don't know if that actually means more or less, so I'll say I like it. Think I'll need another big man though, and hoping I can nab one on the wire. I think I'm definitely a playoff team and will be in the RCL running.


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Elk Grove VillageHOME Menu
On April 8, 2014, Elk Grove Village passed Ordinance No. 3377 that created a new Crime Free Multi-Housing Program. The program was implemented in order to protect all Elk Grove Village residents' safety, and reduce the chances of rental properties causing deleterious effects on adjacent properties, property values and owners.
The Elk Grove Village Crime Free Multi-Housing Program (CFMH) is designed to enable tenants, owners, and managers of rental properties to reduce activities on their property which negatively impact quality of life. Single family rental properties and multi-housing residential properties are included within the program.
Homeowner's associations can adopt the CFMH to positively effect the rental unit portions of their association. This simple and direct program is solution oriented, and has been proven highly effective if the guidelines are followed.
The program utilizes a three-part approach that ensures the crime prevention goal, while maintaining a tenant-friendly approach.
Phase #1: Requires property owners, managers or agents to attend a training seminar presented by the Elk Grove Village Police Department.
Topics Include: Crime Prevention, benefits of applicant screening, crime free lease addendum, evictions, and pro-active management techniques (The Elk Grove Village Ordinance requires owners to at least implement Phase #1 and pass an inspection to be rental license compliant). See planned seminar dates at the bottom of this webpage.
Phase #2: Requires the property owner, manager or agent to meet the minimum security requirements of Crime Prevention Through Environmental Design (CPTED) certification. CPTED Reviews security requirements necessary to enhance tenant safety.
Phase #3: Requires the property owner, manager or agent to hold a tenant crime prevention meeting to obtain full-certification. The property owner, manager or agent will be provided with materials and assistance to publicize the meeting with their tenants.
Upon completion of Phase #1, Phase #2, and Phase #3, property owners, managers, or agents may post signs on their property indicating participation in the Elk Grove Village Crime Free Multi-Housing Program. Additionally, they will be granted the use of the CFMH program logo in their brochures and advertisements.
• A stable, more satisfied tenant base
• Increased demand for rental units
• Lower maintenance and repair costs
• Increased property values
• Improved personal safety for tenants, landlords and managers
What are my next steps?
Step 1 - Complete the Application and Pay the Associated Fee
Crime Free Multi-Housing Application
Completed applications should be mailed to:
ATTN: Finance Department
901 Wellington Avenue
Elk Grove Village, IL 60007
Fee Schedule:
Multiple Dwelling Rental Units (Apartments) - $50/unit
Single Family Residence - $100
Multi-Family Rental Dwelling (Condos/Townhomes) - $100/unit
Individual Room within Single Family Residence - $50
Re-inspection fees apply after two (2) failed inspections.
Step 2 - Complete Crime Free Multi-Housing Training
Property owners, managers or agents must complete training by reading through the presentation, passing a short quiz regarding the material in the presentation, and return a signed lease addendum to the Crime Prevention unit. Once these steps are complete, you will receive a certificate of completion of the Crime Free Multi-Housing program via email. Please use the links below to complete these steps:
- A. Click here to review Seminar Training
- B. Click here to take exam
- C. Click here to print out Lease Addendum, sign it and return to Crime Prevention at email@example.com or via fax at 847-357-4144 or via mail at 901 Wellington Ave, Elk Grove Village, IL 60007.
Step 3 - Have Your Rental Property Inspected
Letters will be mailed to inform you when your area of the Village is designated for inspection and will include instructions to schedule your property inspection. For a copy of the inspection checklists: Click here for Exterior Checklist and Click here for the Interior Checklist.
Upon successful completion of steps 1, 2 and 3, you will receive your rental license.
To schedule your rental inspection, please contact Mike Racanelli at 847-357-4239.
Crime Free Multi-Housing Application
Crime Free Multi-Housing Frequently Asked Questions (FAQ)
Inspection Checklist - Exterior
Inspection Checklist - Interior
For additional information, please contact the Elk Grove Village Police Department at (847) 357-4151 or (847) 357-4152.


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text: any idea how to delete all the blank pages? Amber Book blog: http://community.livejournal.com/book_cuddler/ I have accepted a seat in the House of Representatives, and thereby have consented to my own ruin, to your ruin, and to the ruin of our children. I give you this warning that you may prepare your mind for your fate. John Adams E-Mail: amber.wallens@xxxxxxxxx


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Whismur is very timid. If it starts to cry loudly, it becomes startled by its own crying and cries even harder. When it finally stops crying, the Pokémon goes to sleep, all tired out.
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Read the opposing arguments from Babe Ruthless and Bleacher Fan.
We got things back up and running this week with a passionate debate about Terrelle Pryor and his possible future in the NFL. Pryor is a polarizing athlete who most folks have a solid opinion about one way or another in regards to his future, and his immediate future apparently includes a spot in the upcoming NFL supplemental draft. Most sane people believe he isn't worth a first round pick (Drew Rosenhaus obviously is not included in this group), but that was not the focus of this debate.
I asked Babe Ruthless and Bleacher Fan to debate, if they were NFL general managers, whether or not Pryor was worthy of a roster spot on an NFL team.
Bleacher Fan has had somewhat of a front row seat to Pryor over the past three years (being a resident of Ohio), beginning with Pryor putting off his announcement of where he would take his talents until over a month after signing day. Bleacher Fan effectively paints Pryor as a spoiled, selfish, prima donna, which is the impression that many fans outside of Buckeye Nation possibly already had of Pryor before all of this controversy started.
Putting aside the character issues, Bleacher Fan then questions the impact Pryor can make on an NFL team. Some NFL analysts are not sure he can cut it as a quarterback, so there is speculation thanks to his size he may line up at another position (possibly a receiver). That is because his passing numbers, despite his win-loss record, pale in comparison to some of his contemporaries.
Babe Ruthless, though, commends Pryor on his hiring of Rosenhaus (who exemplifies what Babe is all about), and really simplifies the debate by saying that there isn't much risk by offering a mid to low round pick for Pryor.
Babe also questions how Pryor all of a sudden got labeled as being such a bad guy. Sure, he is the face of this ongoing Ohio State scandal, and he has driven all kinds of various cars from a local dealership, including this beauty that he drove to one of the last meeting he attended as a member of the Ohio State football team. He is going to have that stigma attached to him probably for the rest of his life. But there are worse things.
I count myself as someone who wasn't necessarily in Pryor's corner during his time in Columbus. I was never blown away by his numbers, or by his performance on the field as a quarterback. I nonetheless recognized, and still recognize, the fact that the guy is a tremendous athlete.
It is also important to note that with the ongoing NFL lockout (perhaps you've heard), there hasn't been any OTA's or any contact between players and coaches (at least on record). However, there have been workouts organized by the players. And while rookies such as Cam Newton have been getting acquainted with their teammates and are adjusting quite well, we really don't know what kind of shape Pryor is in. He could be out of shape, or he could be in excellent shape, since he spent much of the spring running from the NCAA (BA-ZING)! All we have to go on is a few tweets from Chad Ochocinco, and is that supposed to be convincing?
What helped decide the debate, though, was a point raised by Babe Ruthless – What is there really to lose on taking Pryor, if it is done with a low pick? Obviously, no team is going to risk a high pick on this "project," but why not a sixth or seventh-round pick? Is next year's draft outlook for any team severely altered by losing a seventh round pick? It's a LOW risk HIGH reward move, and because of that, Babe Ruthless wins the debate.
Terrelle Pryor would be worth a low round supplemental pick, and should be offered a chance to come in and compete for a roster spot. If he makes it, there will be all kind of extra publicity for the team. If not, then there is no big loss.


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|Old World Investments, LLC buys dilapidated
houses in South Charleston, WV and renovates
them into quaint and charming homes. Once
complete, the homes are offered for rent or
sale. These homes are frequently rented
before renovations are complete, so potential
renters must apply early.
We attract the best tenants in the market because we
offer the best rentals on the market !!!
Visit our "Properties" page to find out what's
Old World Investments LLC
Jennifer McQuerrey
Owner of Old World Investments, LLC
|Feature story on Old World Investments, LLC
by LauraBree Austin of WSAZ NewsChannel 3