SGER: Sense in Context: Construction of a Selectional Context Dictionary for English Clauses

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ABSTRACTThis research explores a novel framework for constructing a context dictionary for natural language; that is, a computational lexical database of selectional contexts for argument-taking predicators in the language. The goal of this project is to semi-automatically identify, from very large corpora of the language, the norms of word usage, and then to encode these norms as selectional contexts using a restricted system of semantic types and syntactic identifiers.A novel methodology, called Corpus Pattern Analysis (CPA), which is quite different from established methods in LKB construction, will be used. Rather than building MRD-seeded lexicons, hand-crafted lexicons (WordNet, EuroWordNet), and corpus-informed lexicons (FrameNet), this work will employ a methodology for building a "corpus-driven" lexicon, constrained by specific linguistic modeling principles. Such a "selectional context dictionary" overcomes many of the shortcomings of previous LKB approaches by capturing a richer level of selectional context for predicates in the language. This is possible with the use of shallow parsing of predicate arguments, and the assignment of types from a shallow semantic type system. The technique will be evaluated the selectional discriminatory capabilities of the results, according to established methods; namely, precision and recall measurements against an annotated test set of unseen text.