A Corpus-Based Approach for Building Semantic Lexicons
<|reference_start|>A Corpus-Based Approach for Building Semantic Lexicons: Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.<|reference_end|>
@article{riloff1997a,
    title={A Corpus-Based Approach for Building Semantic Lexicons},
    author={Ellen Riloff and Jessica Shepherd (University of Utah)},
    journal={arXiv preprint arXiv:cmp-lg/9706013},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9706013},
    primaryClass={cmp-lg cs.CL}
}