Verbal polysemy resolution through contextualized clustering of arguments
James D. Pustejovsky, Anna Rumshisky · 2009
Natural language is characterized by a high degree of polysemy, and the majority of content words accept multiple interpretations. However, this does not significantly complicate natural language understanding. Native speakers rely on context to assign the correct sense to each word in an utterance. NLP applications, such as automated word sense disambiguation, require the ability to identify correctly context elements that activate each sense. Our goal in this work is to address the problem of contrasting semantics of the arguments as the source of meaning differentiation for the predicate. We investigate different factors that influence the way sense differentiation for predicates is accomplished in composition and develop a method for identifying semantically diverse arguments that activate the same sense of a polysemous predicate. The method targets specifically polysemous verbs, with an easy extension to other polysemous words. The proposed unsupervised learning method is completely automatic and relies exclusively on distributional information, intentionally eschewing the use of human-constructed knowledge sources and annotated data. We develop the notion of selectional equivalence for polysemous predicates and propose a method for contextualizing the representation of a lexical item with respect to the particular context provided by the predicate. We also present the first attempt at developing a sense-annotated data set that targets sense distinctions dependent predominantly on semantics of a single argument as the source of disambiguation for the predicate. We analyze the difficulties involved in doing semantic annotation for such task. We examine different types of relations within sense inventories and give a qualitative analysis of the effects they have on decisions made by the annotators, as well as annotator error. The developed data set is used to evaluate the quality of the proposed clustering method. The output is adapted for evaluation within a standard sense induction paradigm. We use several evaluation measures to assess different aspects of the algorithm’s performance. Relative to the baselines, we outperform the best systems in the recent SEMEVAL sense induction task (Agirre et al., 2007) on two out of three measures. We also discuss further extensions and possible uses for the proposed automatic algorithm, including the identification of selectional behavior of complex nominals (Pustejovsky, 1995) and the disambiguation of noun phrases with semantically weak head nouns.