Word Sense Disambiguation
Edited by Eneko Agirre and Philip Edmonds

Chapter 1: Introduction

Eneko Agirre, Philip Edmonds

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1.1 Word sense disambiguation. 1

1.2 A brief history of WSD research. 4

1.3 What is a word sense?. 8

1.4 Applications of WSD.. 10

1.5 Basic approaches to WSD.. 12

1.6 State-of-the-art performance. 14

1.7 Promising directions. 15

1.8 Overview of this book. 19

1.9 Further reading. 21

References. 22

1.1 Word Sense Disambiguation

Anyone who gets the joke when they hear a pun will realize that lexical ambiguity is a fundamental characteristic of language: Words can have more than one distinct meaning. So why is it that text doesn't seem like one long string of puns? After all, lexical ambiguity is pervasive. The 121 most frequent English nouns, which account for about one in five word occurrences in real text, have on average 7.8 meanings each (in the Princeton WordNet (Miller 1990), tabulated by Ng and Lee (1996)). But the potential for ambiguous readings tends to go completely unnoticed in normal text and flowing conversation. The effect is so strong that some people will even miss a pun (a real ambiguity) obvious to others. Words may be polysemous in principle, but in actual text there is very little real ambiguity - to a person.

Lexical disambiguation in its broadest definition is nothing less than determining the meaning of every word in context, which appears to be a largely unconscious process in people. As a computational problem it is often described as "AI-complete", that is, a problem whose solution presupposes a solution to complete natural-language understanding or common-sense reasoning (Ide and Véronis 1998).

In the field of computational linguistics, the problem is generally called word sense disambiguation (WSD), and is defined as the problem of computationally determining which "sense" of a word is activated by the use of the word in a particular context. WSD is essentially a task of classification: word senses are the classes, the context provides the evidence, and each occurrence of a word is assigned to one or more of its possible classes based on the evidence. This is the traditional and common characterization of WSD that sees it as an explicit process of disambiguation with respect to a fixed inventory of word senses. Words are assumed to have a finite and discrete set of senses from a dictionary, a lexical knowledge base, or an ontology (in the latter, senses correspond to concepts that a word lexicalizes). Application-specific inventories can also be used. For instance, in a machine translation (MT) setting, one can treat word translations as word senses, an approach that is becoming increasingly feasible because of the availability of large multilingual parallel corpora that can serve as training data. The fixed inventory of traditional WSD reduces the complexity of the problem, making it tractable, but alternatives exist, as we will see below.

WSD has obvious relationships to other fields such as lexical semantics, whose main endeavour is to define, analyze, and ultimately understand the relationships between "word", "meaning", and "context". But even though word meaning is at the heart of the problem, WSD has never really found a home in lexical semantics. It could be that lexical semantics has always been more concerned with representational issues (see, for example, Lyons 1995) and models of word meaning and polysemy so far too complex for WSD (Cruse 1986; Ravin and Leacock 2000). And so, the obvious procedural or computational nature of WSD paired with its early invocation in the context of machine translation (Weaver 1949) has allied it more closely with language technology and thus computational linguistics. In fact, WSD has more in common with modern lexicography, with its intuitive premise that word uses group into coherent semantic units and its empirical corpus-based approaches, than with lexical semantics (Wilks et al. 1993).

The importance of WSD has been widely acknowledged in computational linguistics; some 700 papers in the ACL Anthology mention the term "word sense disambiguation". [Footnote 1: To compare, "anaphora resolution" occurs in 438 papers; however, such statistics should not be taken too seriously. The ACL Anthology is a digital archive of research papers in computational linguistics, covering conferences and workshops from 1979 to the present, maintained by the Association for Computational Linguistics (www.aclweb.org/anthology). Our statistics were gathered in November 2005.] Of course, WSD is not thought of as an end in itself, but as an enabler for other tasks and applications of computational linguistics and natural language processing (NLP) such as parsing, semantic interpretation, machine translation, information retrieval, text mining, and (lexical) knowledge acquisition. However, in counterpoint to its theoretical importance, explicit WSD has not always demonstrated benefits in real applications.

A long-standing and central debate is whether WSD should be researched as a generic or as an integrated component. In the generic setting, the WSD component is a black box encompassing an explicit process of WSD that can be dropped into any application, much like a part-of-speech tagger or a syntactic parser. The alternative is to include WSD as a task-specific "component" of a particular application in a specific domain and integrated so completely into a system that it is difficult to separate out. Research into explicit WSD, having received the bulk of effort, has progressed steadily and successfully to a point where some people now question if the upper limit in accuracy (low as it is on fine-grained sense distinctions) has been attained (Section 1.6 gives current performance levels). And yet, explicit WSD has not yet been convincingly demonstrated to have a significant positive effect on any application. Only the integrated approach has been successful, with disambiguation often occurring implicitly by virtue of other operations, for example, in the language and translation models of statistical machine translation. The former conception is easier to define, experiment with, and evaluate, and is thus more amenable to the scientific method; the latter is more applicable and puts the need for explicit WSD into question.

Despite uncertain results on real applications, the effort on explicit WSD has produced a solid legacy of research results, methodology, and insights for computational semantics. For example, local contextual features (i.e., other words near the target word) provide better evidence in general than wider topical features (Yarowsky 2000). Indeed, the role of context in WSD is much better understood: Compared to other classification tasks in NLP (such as part-of-speech tagging), WSD requires a wide range of contextual knowledge to be modeled from fixed patterns of part-of-speech tags around a topic word to syntactic relations to topical and domain associations. Each part-of-speech and even each word relies on different types of knowledge for disambiguation. For instance, nouns benefit from a wide context and local collocations, whereas verbs benefit from syntactic features. Some words can be disambiguated by a single feature in the right position, benefiting from a "discriminative" method; others require an aggregation of many features. Homographs are generally much easier to disambiguate than polysemous words. [Footnote 2: For the present purposes, a homograph is a coarse-grained sense distinction between often completely unrelated meanings of the same word string (e.g., bank as a financial institution or a river side). Polysemy involves a finer-grained sense distinction in which the senses can be related in different ways (e.g., bank as a physical building or as an institution). See Section 1.3 for further details.] An evaluation methodology has been defined by Senseval (Kilgarriff and Palmer 2000) and many resources in several languages are now available. Finally, for a small sample of tested words, that have sufficient training data, the performance of WSD systems is comparable to that of humans (measured as the inter-tagger agreement among two or more humans), as demonstrated by the recent Senseval results (see Sect. 1.6 below).

Two "spin offs" worth mentioning include the development of explicit WSD as a benchmark application for machine learning research, because of the clear problem definition and methodology, the variety of problem spaces (each word is a separate classification task), the high-dimensional feature space, and the skewed nature of word sense distributions. And second, WSD research is helping in the development of popular lexical resources such as WordNet (Fellbaum 1998; Palmer et al. 2001, 2006) and the multilingual lexicons of the MEANING project (Vossen et al. 2006).

To introduce the topic of WSD, we begin with a brief history. Then, in Section 1.3 we discuss the central theoretical issues of "word sense" and the sense inventory. In Sections 1.4-1.6 we summarize several practical aspects including applicability to NLP tasks, the three basic approaches to WSD, and current performance achievements. Finally, Section 1.7 gathers our thoughts on emerging and future research into WSD.

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