Building a Collocational Semantic Lexicon
نویسنده
چکیده
Natural Language Generation (NLG) systems require access to collocational information to help determine lexical choices constrained both by syntactic and semantic concerns. Constructing linguistic resources to support these decisions can be time-consuming whereas, if the information is extracted automatically, data sparsity limits the variety of the output. This paper reports on a method for extracting collocational data from the British National Corpus, and then generalizing it using WordNet to tackle the sparsity problem. The method is evaluated using the lexical choice component of ENIGMA, an NLG system that generates cryptic crossword clues.
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