WSD based on mutual information and syntactic patterns

WSD Based on Mutual Information and Syntactic Patterns David Fern´andez-Amor´os Departamento de Lenguajes y Sistemas Inform´aticos UNED david@lsi.uned.es Abstract This paper describes a hybrid system for WSD, pre- sented to the English all-words and lexical-sample tasks, that relies on two different unsupervised ap- proaches. The first one selects the senses according to mutual information proximity between a context word a variant of the sense. The second heuristic analyzes the examples of use in the glosses of the senses so that simple syntactic patterns are inferred. This patterns are matched against the disambigua- tion contexts. We show that the first heuristic ob- tains a precision and recall of .58 and .35 respec- tively in the all words task while the second obtains .80 and .25. The high precision obtained recom- mends deeper research of the techniques. Results for the lexical sample task are also provided. 1 Introduction We will describe in this paper the system that we presented to the SENSEVAL-3 competition in the English all-words and lexical-sample tasks. It is an unsupervised system that relies only on dictionary information and raw coocurrence data that we col- lected from a large untagged corpus. There is also a supervised extension of the system for the lexical sample task that takes into account the training data provided for the lexical sample task. We will de- scribe two heuristics; the first one selects the sense of the words’ synset with a synonym with the high- est Mutual Information (MI) with a context word. This heuristic will be covered in section 2. The sec- ond heuristic relies on a set of syntactic structure rules that support particular senses. This rules have been extracted from the examples in WordNet sense glosses. Section 3 will be devoted to this technique. In section 4 we will explain the combination of both heuristics to finish in section 5 with our conclusions and some considerations for future work. 2 Selection of the closest variant In the second edition of SENSEVAL, we presented a system, described in (Fern´andez-Amor´os et al., 2001), that assigned scores to each word sense adding up Mutual Information estimates between all the pairs (word-in-context, word-in-gloss). We have identified some problems with this technique. • This exhaustive use of the mutual information estimates turned out to be very noisy, given that the errors in the individual mutual information estimates often correlated, thus affecting the fi- nal score for a sense. • Sense glosses usually contain vocabulary that is not particularly relevant to the specific sense. • Another typical problem for unsupervised sys- tems is that the sense inventory contains many senses with little or no presence in actual texts. This last problem has been addressed in a very straightforward manner, since we have dis- carded the senses for a word with a relative fre- quency below 10%. The first problem might very well improve by it- self when larger untagged corpora are available and increasing computing power eliminates the need for a limited controlled vocabulary in the MI …

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