The use of semantic resources is common in modern NLP systems, but methods to extract lexical semantics have only recently begun to perform well enough for practical use. We evaluate existing and new similarity metrics for thesaurus extraction, and experiment with the tradeoff between extraction performance and efficiency. We propose an approximation algorithm, based on canonical attributes and coarse-and fine-grained matching, that reduces the time complexity and execution time of thesaurus extraction with only a marginal performance penalty.
Curran REF evaluated existing and new similarity metrics and then proposed a method, called an approximation algorithm, to improve the time complexity and execution time of similarity calculation.
Improvements In Automatic Thesaurus Extraction
Workshop On Unsupervised Lexical Acquisition
Computer Science