Using Similarity Measures to Extend the LinGO Lexicon

Abstract

Deep processing of natural language requires large scale lexical resources that have sufficient coverage at a sufficient level of detail and accuracy (i.e. both recall and precision). Hand-crafted lexicons are extremely labour-intensive to create and maintain, and require continuous updating and extension to retain their level of usability. In this paper we present a technique for extending lexicons using similarity measures that can be extracted from corpora. The technique involves creating lexical entries for unknown words based on entries for words that are known and that are deemed to be distributionally similar. We demonstrate the applicability of the approach by providing an extended lexicon for the LinGO system using similarity measures extracted from the BNC. We also discuss the advantages and disadvantages of using such lexical extensions in different ways: principally either as part of the main lexicon or as a separate resource used only for “last resort” use.

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