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Combining multiple features for single- word term extractionстатья
- Авторы: Nokel M.A., Bolshakova E.I., Loukachevitch N.V.
- Журнал: Компьютерная лингвистика и интеллектуальные технологии (Computational Linguistics and Intellectual Technologies)
- Год издания: 2012
- Издательство: Редколлегия сборника «Компьютерная лингвистика и интеллектуальные технологии»
- Местоположение издательства: Москва
- Первая страница: 490
- Последняя страница: 501
- Аннотация: The paper describes experiments on automatic single-word term extraction based on combining various features of words, mainly linguistic and statistical, by machine learning methods. Since single-word terms are much more difficult to recognize than multi-word terms, a broad range of word features was taken into account, among them are widely-known measures (such as TF-IDF), some novel features, as well as proposed modifications of features usually applied for multi-word term extraction. A large target collection of Russian texts in the domain of banking was taken for experiments. Average Precision was chosen to evaluate the results of term extraction, along with the manually created thesaurus of terminology on banking activity that was used to approve extracted terms. The experiments showed that the use of multiple features significantly improves the results of automatic extraction of domain-specific terms. It was proved that logistic regression is the best machine learning method for single- word term extraction; the subset of word features significant for term extraction was also revealed.
- Добавил в систему: Лукашевич Наталья Валентиновна