Machine Learning for Domain-Adaptive Word Sense Disambiguation
نویسندگان
چکیده
This paper investigates the use of machine learning techniques for word sense disambiguation. The aim is to improve on the performance of general-purpose methods, by making the disambiguation method adaptable to new domains. Results are presented here for two different test cases: financial news from the Wall Street Journal, extracted from the SEMCOR corpus, and general-theme news from the same corpus. The two experiments show that the adaptive disambiguation method can achieve high recall and precision; more so in the restricted domain of financial news than in the general-theme case.
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تاریخ انتشار 1999