Semantic separator learning and its applications in unsupervised Chinese text parsing

  • Authors:

  • Yuming Wu;Xiaodong Luo;Zhen Yang

  • Affiliations:

  • Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University of the Chinese Academy of Scienc ...;China Telecom Corporation Limited Shanghai Branch, Shanghai, China 200120;Shanghai Research Institute of China Telecom Corporation Limited, Shanghai, China 200120

  • Venue:
  • Frontiers of Computer Science: Selected Publications from Chinese Universities
  • Year:
  • 2013

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Abstract

Grammar learning has been a bottleneck problem for a long time. In this paper, we propose a method of semantic separator learning, a special case of grammar learning. The method is based on the hypothesis that some classes of words, called semantic separators, split a sentence into several constituents. The semantic separators are represented by words together with their part-of-speech tags and other information so that rich semantic information can be involved. In the method, we first identify the semantic separators with the help of noun phrase boundaries, called subseparators. Next, the argument classes of the separators are learned from corpus by generalizing argument instances in a hypernym space. Finally, in order to evaluate the learned semantic separators, we use them in unsupervised Chinese text parsing. The experiments on a manually labeled test set show that the proposed method outperforms previous methods of unsupervised text parsing.