Target word detection and semantic role chunking using support vector machines

  • Authors:

  • Kadri Hacioglu;Wayne Ward

  • Affiliations:

  • University of Colorado at Boulder;University of Colorado at Boulder

  • Venue:
  • NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
  • Year:
  • 2003

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Abstract

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 tasks, we present encouraging results of experiments carried out using the annotated FrameNet database.