Sense Disambiguation Using Semantic Relations and Adjacency Information

Sense Disambiguation Using Semantic Relations and Adjacency Information Anil S. Chakravarthy MIT Media Laboratory 20 Ames Street E15-468a Cambridge MA 02139 anil @ media.mit.edu Abstract This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic rela- tions extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. In all, the disam- biguator uses 39 heuristics based on 12 relationships. 1 Introduction Word-sense disambiguation has long been recognized as a difficult problem in computational linguistics. As early as 1960, Bar-Hillel [1] noted that a computer program would find it challenging to recognize the two different senses of the word "pen" in "The pen is in the box," and "The box is in the pen." In recent years, there has been a resurgence of interest in word-sense disambiguation due to the availability of linguistic resources like dictionar- ies and thesauri, and due to the importance of disambig- uation in applications like information retrieval and machine translation. The task of disambiguation is to assign a word to one or more senses in a reference by taking into account the context in which the word occurs. The reference can be a standard dictionary or thesaurus, or a lexicon con- structed specially for some application. The context is provided by the text unit (paragraph, sentence, etc.) in which the word occurs. The disambiguator described in this paper is based on two reference sources, the Webster's Seventh Dictionary and the semantic thesaurus WordNet [12]. Before the disambiguator is applied, the text input is processed first by a part-of-speech tagger and then by a phrase extrac- tor which detects phrase boundaries. Therefore, for each ambiguous word, the disambiguator knows the part of speech, and other phrase headwords and modifiers that are adjacent to it. Based on this context information, the disambiguator uses a set of heuristics to assign one or more senses from the Webster's dictionary or WordNet to the word. Here is an example of a heuristic that relies on the fact that conjoined head nouns are likely to refer to objects of the same category. Consider the ambiguous word "snow" in the sentence "Slush and snow filled the roads." In this sentence, the tagger identifies "snow" as a noun. The phrase extractor indicates that "snow" and "slush" are conjoined head words of a noun phrase. Then, the heuristic uses WordNet to identify the senses of "slush" and "snow" that belong to a common cate- gory. Therefore, the sense of "snow" as "cocaine" is dis- carded by this heuristic. The disambiguator has been incorporated into two infor- mation retrieval …

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