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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.1
- 수록면
- 261 - 286 (26page)
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This paper reports on novel results for the automatic classification of semantic relations that hold between the constituents of nominal compounds in German. It utilizes a hybrid annotation scheme that models semantic relations using a combination of prepositional paraphrases and semantic properties. The machine learning (ML) experiments use the support vector machine (SVM) implementation in Weka for single-label prediction tasks and Weka SVMs in conjunction with the Mulan library for multi-label prediction.
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참고문헌 (27)
참고문헌 신청
Ben-Hur, A. / 2010 / A user's guide to support vector machines / Methods in molecular biology (609) : 23 ~ 39
Caruana, R. / 1997 / Multitask Learning / Machine learning 75 (1) : 41 ~ 75
Cohen, J. / 1960 / A coefficient of agreement of nominal scales / Educational and Psychological Measurement 20 (1) : 37 ~ 46
Dima, C. / 2014 / How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations / Proceedings of the Ninth International Conference on Language Re
Dima, C. / 2015 / Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings / 11th International Conference on Computational Semantics (IWCS 2015)
Caruana, R. / 1997 / Multitask Learning / Machine learning 75 (1) : 41 ~ 75
Cohen, J. / 1960 / A coefficient of agreement of nominal scales / Educational and Psychological Measurement 20 (1) : 37 ~ 46
Dima, C. / 2014 / How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations / Proceedings of the Ninth International Conference on Language Re
Dima, C. / 2015 / Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings / 11th International Conference on Computational Semantics (IWCS 2015)
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