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Journal Article

Semantic Role Labeling Using Maximum Entropy

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2004) 3314 954-961
DOI: 10.1007/978-3-540-30497-5_147
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Abstract

In this paper, semantic role labeling is addressed. We formulate the problem as a classification task, in which the words of a sentence are assigned to semantic role classes using a classifier. The maximum entropy approach is applied to train the classifier, by using a large real corpus annotated with argument structures. © Springer-Verlag 2004.

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APA

Lan, K. C., Ho, K. S., Luk, R. W. P., & Leong, H. V. (2004). Semantic Role Labeling Using Maximum Entropy. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 954–961. https://doi.org/10.1007/978-3-540-30497-5_147

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