Human Language Technologies (HLT) need dictionaries, to tell them what words mean and how they behave. People making dictionaries (lexicographers) need HLT, to help them identify how words behave so they can make better dictionaries. Thus a potential for synergy exists across the range of lexical data - in the construction of headword lists, for spelling correction, phonetics, morphology and syntax, but nowhere more than for semantics, and in particular the vexed question of how a word's meaning should be analysed into distinct senses. HLT needs all the help it can get from dictionaries, because it is a very hard problem to identify which meaning of a word applies. Lexicographers need all the help they can get because the analysis of meaning is the second hardest part of their job (Kilgarriff, 1998), it occupies a large share of their working hours, and it is one where, currently, they have very little to go on beyond intuition and other dictionaries.
|Title of host publication||Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics|
|Place of Publication||NJ, USA|
|Publisher||Association for Computational Linguistics|
|Number of pages||4|
|Publication status||Published - 2003|
Kilgarriff, A., Evans, R., Koeling, M., Rundell, M., & Tugwell, D. (2003). WASPBENCH: a lexicographer's workbench supporting state-of-the-art word sense disambiguation. In Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics (Vol. 2, pp. 211-214). Association for Computational Linguistics.