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NAACL
2003

Target Word Detection and Semantic Role Chunking using Support Vector Machines

8 years 11 months ago
Target Word Detection and Semantic Role Chunking using Support Vector Machines
In this paper, the automatic labeling of semantic roles in a sentence is considered as a chunking task. We define a semantic chunk as the sequence of words that fills a semantic role defined in a semantic frame. It is straightforward to convert chunking into a tagging task using one of several IOB representations. Using this representation each word is tagged with I, which means that the word is inside a chunk, or with O, which means that the word is outside a chunk, or B, which means that the word is the beginning of a chunk. Tagging can also be seen as a multi-class classification problem. After recasting the multi-class problem as a number of binary-class problems, we use support vector machines to implement the binary classifiers. We explore two semantic chunking tasks. In the first task we simultaneously detect the target word and segments of semantic roles. In the second task, in addition, we label the semantic segments with their respective semantic role types. For both t...
Kadri Hacioglu, Wayne Ward
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Kadri Hacioglu, Wayne Ward
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