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Learning Generalized Features for Semantic Role Labeling

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Published:12 April 2016Publication History
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Abstract

This article makes an effort to improve Semantic Role Labeling (SRL) through learning generalized features. The SRL task is usually treated as a supervised problem. Therefore, a huge set of features are crucial to the performance of SRL systems. But these features often lack generalization powers when predicting an unseen argument. This article proposes a simple approach to relieve the issue. A strong intuition is that arguments occurring in similar syntactic positions are likely to bear the same semantic role, and, analogously, arguments that are lexically similar are likely to represent the same semantic role. Therefore, it will be informative to SRL if syntactic or lexical similar arguments can activate the same feature. Inspired by this, we embed the information of lexicalization and syntax into a feature vector for each argument and then use K-means to make clustering for all feature vectors of training set. For an unseen argument to be predicted, it will belong to the same cluster as its similar arguments of training set. Therefore, the clusters can be thought of as a kind of generalized feature. We evaluate our method on several benchmarks. The experimental results show that our approach can significantly improve the SRL performance.

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          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 15, Issue 4
          June 2016
          173 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/2915955
          Issue’s Table of Contents

          Copyright © 2016 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 April 2016
          • Revised: 1 January 2016
          • Accepted: 1 January 2016
          • Received: 1 August 2015
          Published in tallip Volume 15, Issue 4

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