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The Grammar of Sense: Is word-sense tagging much more than part-of-speech tagging?

This squib claims that Large-scale Automatic Sense Tagging of text (LAST) can be done at a high-level of accuracy and with far less complexity and computational effort than has been believed until now. Moreover, it can be done for all open class words, and not just carefully selected opposed pairs as in some recent work. We describe two experiments: one exploring the amount of information relevant to sense disambiguation which is contained in the part-of-speech field of entries in Longman Dictionary of Contemporary English (LDOCE). Another, more practical, experiment attempts sense disambiguation of all open class words in a text assigning LDOCE homographs as sense tags using only part-of-speech information. We report that 92% of open class words can be successfully tagged in this way. We plan to extend this work and to implement an improved large-scale tagger, a description of which is included here.


Introduction
Large-scale Automatic Sense Tagging (LAST) has become, like part-of-speech tagging and automatic parsing before it, an important and much researched intermediate task in natural language processing.
By "intermediate" we mean a task whose evaluation is determined by some linguistic or theoretical criterion, as opposed to a task like machine translation (MT) or information extraction (IE), where the criteria can be judged by end-users of information, rather than those familiar only with linguistic notations. This is an important distinction, even if sometimes hard to make firm, but corresponds to a commonsense intuition that no one wants part-of-speech tagging, parsing or LAST information as such, but only as a means to some other end, unless, of course, the aim is to verify or refute a theory of language processing or structure, where intermediate information is often essential.
It is this difference of goals which has allowed intermediate tasks to become so prominent in our field, sometimes at the expense of "final" tasks, since, as researchers, we are naturally more preoccupied with the confirmation and refutation of theories than with the provision of usable output. Depending on our particular taste in theories, we tend therefore to elevate the status of certain intermediate results because we believe, on a priori grounds, that they are essential to the achieving of final goals. So, for example, the importance of assessable large-scale corpus parsing has always been more important to those who believe it to be an important step to tasks like MT than to those who do not.
It has always been a cliché of MT that the problem of word-sense ambiguity was one of the intractable problems that has slowed progress in achieving high quality MT, and that observation can be taken as justifying attempts to achieve LAST. It remains to be seen whether high achievement at any of these three intermediate tasks (now in sight in all cases) does in fact raise the quality of MT and IE.
(The authors are currently working on a project which, in part, seeks to discover how useful sense disambiguation will be for IE).
In the case of LAST, there is an additional problem about the objectivity and assessability of the task, since the notion of word-sense has proved more dubious and hard to make precise than that of part-of-speech tag or syntactic category. In both those cases there is broad agreement that, although there is a range of label sets available, the sets are broadly mappable to each other, and that, whatever the labels used, there is sufficient inter-subjective agreement on syntactic structure.
In the case of word-senses, there has not been such a consensus: it is often observed that dictionaries may classify the senses of a given word in different ways and that the sense classifications may be incommensurable, in that there may be no mapping in terms of set inclusion between the differing sense sets for a word in different dictionaries. There is also the homograph problem: lexicographers divide a word's usages into homographs and senses (where homographs tend to be supersets of senses) in a way that resists clear analysis. The normal explanation of homograph (that it is really a different word that just happens to be spelled the same as another, in which sense bank-for-money and bankof-river are often deemed homographs in English) does not allow one to decide, on etymological or any other evidence, whether one is dealing with a homographic or sense distinction.
Again, lexicographers are known to divide roughly between "lumpers" and "splitters": those who like to divide senses without apparent end, and those who prefer larger (more "homographic") clusters of usages. All this has led some to despair and call it all sense-distinction, some going even so far as to say that words have more or less one sense each (e.g. [12]). Kilgarriff [6] has argued that human subjects cannot in fact assign sense-tags to words in corpora, which, if true would make LAST a pointless enterprise. We have answered that claim elsewhere [13], but it can leave others feeling that, at best, LAST can only produce a tagging circular upon a particular dictionary, the one that contains the sense tagging taxonomy used.
The response to that is to turn to the computational work that has attempted to derive sense clusters directly from corpora without any a priori "seeding" of the clusters. Work reported at IBM [2] and by McDonald and Plate (see [14]) using quite different statistical techniques have shown that clusterings correspond closely to a broad (close to homographic) notion of sense. Again, one could cite the work of Itai and Dagon [5] which has shown the consistency of a cross-linguistic notion of wordsense as determined by foreign language equivalents in bilingual texts, e.g. the distribution of "duty" in the Canadian Hansard English texts against aligned French sentences that contain either "devoir" (=duty as obligation) or "impot" (=duty as tax), where the broad distinctions are clear and obvious: the contexts of "duty" correspond to two objectively characterisable contexts in French.

Work So Far
All this leads us to believe that there is a proper intuition underlying LAST, since those results are intersubjective beyond the framework of a particular sense taxonomy in a particular dictionary. Existing LAST work cannot itself confirm that, but let us review it quickly. Three basic methods have been used, corresponding to intuitions of the linkage of word-sense to: 1. syntactic context, usually determined by the window of words in which a token occurs.
2. relevance to subject matter, in the sense of a topic context provided by, say, Roget's thesaurus heads (a method for LAST first explored by Masterman in 1966 (see [14])).
3. overlap of word occurrences within the definitions of the senses to be distinguished, a method first proposed by Lesk [7]. Method 3 has recently been optimised by Cowie and Guthrie [3] using simulated annealing and they report results of 72% correct assignment at the homographic level in LDOCE and a much lower level for individual sense assignment. This result must be seen against a background figure of 62% [14] correct sense assignment in LDOCE achieved by assigning the first 2 LDOCE sense in an entry. However, we suspect that the wrong optimisation function was used in the annealing, one that tended to assign senses with long definitions in LDOCE, and so the figure could have been much better, a matter we intend to remedy later. The importance of this method is that it disambiguates all the content words in a sentence, even though it involved a vast computation for a sentence if all the LDOCE senses were considered, often optimising more than 10 9 sense combinations for a 12 word sentence.
Yarowsky [15] has investigated both methods 1 and 2, and we have criticised his methods elsewhere, pioneering though they are, and achieving figures of up to 96% correct for selected word distinctions [16]. The problems with his method are that it is a very small scale method for numbers of words usually less than 10. Moreover, although he has sought to combine methods, the sense of "sense" used varies (from appearing under a single Roget head to having a bilingual Dagon/Itai style-correspondence). One could generalise and say his results can therefore be compared to Cowie and Guthrie, though they are much superior on the smaller scale he uses, since the distinctions Yarowsky makes (e.g. the Roget and bilingual ones) are equivalent to what are distinguished as homographs in LDOCE.
A key fact to notice about 1-3 is that they are methods resting on quite different intuitions: and one might well infer that, if they all capture at least part of what we intuitively mean by word-sense, then the sensible way to achieve high-quality LAST is to combine all three (Yarowsky uses aspects of 1 and 2). In this squib, we shall show how we achieve high percentage, large scale, figures with a method different from all the above. In the conclusion we shall show how we intend to proceed by combining our current results with aspects of 1, 2, and 3 to optimise our results further.

Starting again at LAST
The observation with which we begin is that POS tagging and LAST are not as independent as has always been assumed. POS tagging [1] is a well established module in many NLP systems these days giving accuracy figures of up to 98%. Our first investigation was to see how far, given a basic NLP lexicon such as a tractable form of LDOCE, accurate POS tagging would discriminate senses without any further processing. The result was far more striking than we expected. 3 The Longman Dictionary of Contemporary English #1 [9] is a dictionary designed for students of English and containing around 36,000 word types. Each word type consists of one or more homographs, the homographs themselves are sets of senses for the word type. Each of the word-senses in LDOCE contains part-of-speech information indicating which grammatical category that sense corresponds to, taken from a set of 17 broad grammatical distinctions. All the senses which make up a homograph have identical part-of-speech information. However, this is not to say that word-senses are partitioned into homographs by syntactic criteria: around 2% of word types in LDOCE contain a homograph which has more than one part-of-speech associated with each of its senses, which is thus a homograph with multiple parts-of-speech. There are also many words, for example "bank", which contain more than one homograph with the same part-of-speech. We argue later that homographs partitioned by grammatical categories are a natural side-effect of grouping semantically related senses.

The Taxonomy of a Lexicon: A Gedankenexperiment
We attempted to discover how useful part-of-speech information could be for semantic disambiguation.
We scanned through LDOCE and examined each word type for possible disambiguation to the homograph level by part of speech. By examining this information it is possible to place each of the LDOCE word types in one of the following categories: 1. Guaranteed disambiguation: those word types for which no grammatical category is associated with more than one homograph.
These words will always be disambiguated if its part-of-speech is known.
eg. a word with 3 homographs with grammatical categories n, v and adj.

2.
Possible disambiguation: those for which there is at least one grammatical category associated with a single homograph but there is another category which is associated with more than one.
These words will be disambiguated by some part-of-speech assignments, but others will not disambiguate it fully. eg. a word whose homographs had grammatical categories n, v, v.

3.
No disambiguation: those for which each grammatical category that can apply to the word type is associated with more than one homograph.
These word can never be fully disambiguated by part-of-speech alone.
eg. homographs with grammatical categories v, v, n, n.
The number of words which fall into the guaranteed disambiguation category puts a lower bound on the number which a perfect part-of-speech tagger could disambiguate, while an upper bound can be found by adding the number which fall into the possible disambiguation category, since these may be disambiguated by the information contained in a part-of-speech tag, although they may not be.
We examined each of the word types in LDOCE (except for closed class words such as prepositions) and found that 34% were polysemous and 12% polyhomographic (a word types must, of course, be polysemous to be polyhomographic since each homograph is a non-empty set of senses). 88% of the polyhomographic words were guaranteed to be disambiguated to the homograph level and 95% of them could possibly be disambiguated to the homograph level. If we assume that all monohomographic words are trivially disambiguated then we can translate these values to 98.5% guaranteed disambiguation and 99.4% possible disambiguation over all word types. This experiment of course presumes a perfect POS tagger but, as we have already mentioned, many fairly reliable taggers are readily available. It is impossible to tell how these results will translate to a real experiment since the results of this will be highly dependent upon the distribution of word types across tokens in the corpus which is being examined. In this Gedankenexperiment each of the word types in the dictionary is considered only once, but some word types will occur many times in a corpus and even more never will. So, for example, the upper bound would not apply if, by chance, none of the words of type 3 appeared in a given corpus.

Using a Tagger: An Aktionexperiment
To test this method in practice we took five articles from the Wall Street Journal, containing around 1700 words in total, and disambiguated the content words using part-of-speech tags as the sole information source.
The texts were POS tagged using the Brill tagger [1] and open class words were flagged (the POS tags were used to decide which words belonged to the open classes). The tags set used by the Brill tagger were manually mapped onto the simpler part-of-speech fields for LDOCE homographs. 4 The LDOCE homographs which corresponded to the part-of-speech assigned by the tagger were extracted from the appropriate LDOCE entry and the first (most frequent) of those was chosen as the sense of the word.
We found that 92% of the content word tokens were tagged with the correct homograph compared with manual tagging of the same five texts. 57% of the open class words were in fact polyhomographic and of these 87.4% were assigned the correct homograph. The monohomographic words, which made up the rest of the open class words were, trivially, 100% correct.
It is perhaps worth noting in passing that although only 12% of the word types in LDOCE are polyhomographic, more than half the content words in an actual text are. This is an indication of the kinds of words which are commonly used in English and is in keeping with Zipf's Law [17].
Our simple and cheap method achieves a much higher result than the computationally intensive

Conclusion
Our result should not be misinterpreted as implying some kind of reduction of semantic matters to syntactic or morphological ones and so to a loss of richness of texture in NLP. First, because grammatical categories are themselves essentially semantic in origin, a fact not contradicted by observing that many languages have inflectional criteria for what it is to be a particular part-of-speech. It is no answer to the question "What is a noun in German?" to answer that it is the part-of-speech that is regularly capitalised!
The commonsense view is that parts-of-speech are rooted in our basic ontology of the world, of how it is, which is in turn a fundamentally semantic matter. In the philosophy of language this view is sometimes thought weakened by observations like those of Waismann [12] that some aspects of the world are captured in one language by the use of one part of speech in one language but by a different part-of-speech in another, which, if true, implies the matter cannot be semantic in the sense of how the world is independent of ourselves and our languages. But this, fortunately for us as NLPers, is a question on which we do not have to have views: it is certainly not an issue that can divide partsof-speech from word-sense as one that separates language from the world, or at least the perceived world.
A more persistent worry this result may exacerbate is the traditional AI view of all these matters, one shared with Bar Hillel, that issues of word-sense were to be settled by world knowledge, not again in any objective sense, but as a function of stored codings that express the state of the world. If that view is right (and many of the authors of say [10] held it in 1988) then, it is unacceptable that a crucial issue like word-sense be settled by matters independent of stored world knowledge.
Matters are not really so depressing, and one way to construe the current result is that low level methods can give a very effective, basic notion of word-sense discrimination, probably close to what we are calling a homograph, and that all finer distinctions, whether one wishes to call them word-sense or not, are matters for world knowledge, which is to say, classic AI. So, one can cite of simple examples such as "He wiped the bicycle before sitting on it." which have been used to argue that there is a sense of bicycle meaning "bicycle seat", and then so on for each of its 250 component parts. This is plainly absurd: an extension of word-sense into an area best thought of as knowledge processing.
The current techniques can be seen as defining (at least when optimised, see below) a limit to the extension of word-sense and thus the demarcating the fields of NLP and AI-proper. This is perhaps no more than a sensible compromise position, consistent with the NLP discrimination methods available.
In conclusion, it is important to stress that our method is comparable to other recent work like Yarowsky's and Cowie & Guthrie: like theirs our method separates senses with respect to an available human-constructed database of substantial size (LDOCE in our case, Roget, Groliers or bilingual text in his) and at about the same level of grain size, namely the LDOCE homograph.

Further work
What we plan now, and which should have produced results before this squib appears, is to pipeline a number of independent sources of sense tagging information together, probably in the following order: 1. LDOCE sense discrimination by POS 2. Subject codes (= LDOCE pragmatic codes) 3. LDOCE examples as correlates 4. Simulated annealing optimisation of Lesk's heuristic.
1 was described here. 2 has been used to produce a sense-tagged hierarchy for all LDOCE nouns at a high level of accuracy [14], and is essentially the same type of information as the Roget Thesaurus used by Yarowsky in [16]. 3 is a limited version of the One-sense-per-collocation heuristic of Yarowsky [15] which he showed had sense resolving power for almost any explicit form of collocation. We propose to use the example sentences in LDOCE (or perhaps the Longman Activator [11]) for each sense as a possible signature correlate. These examples have been much criticised, not being corpus attested, but they are easily available on a large scale and, even if they prove a weak source of information, will be unlikely to harm the sense filtering. 4 is the Cowie and Guthrie method but with a changed optimisation function as we noted earlier. We have been developing GATE, a General Architecture for Text Engineering [4], and expect that to provide the appropriate environment for developing this comprehensive multi-source word-sense discriminator.