Verbal Polysemy Resolution through Contextualized Clustering of Arguments

Verbal Polysemy Resolution through Contextualized Clustering of Arguments A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Anna A. Rumshisky 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