TSD 2004: Text, Speech and Dialogue pp 163-170 | Cite as
Identifying Semantic Roles Using Maximum Entropy Models
Conference paper
Abstract
In this paper, a supervised learning method of semantic role labeling is presented. It is based on maximum entropy conditional probability models. This method acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed for a few words selected from sections of the Wall Street Journal part of the Penn Treebank corpus.
Keywords
Support Vector Machine Natural Language Processing Semantic Role Word Sense Disambiguation Supervise Learning Method
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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