Function Labeling for Unparsed Chinese Text

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  • Yuan Caixia
    Faculty of Engineering, The University of Tokushima School of Computer, Beijing University of Posts and Telecommunications
  • Ren Fuji
    Faculty of Engineering, The University of Tokushima School of Computer, Beijing University of Posts and Telecommunications
  • Wang Xiaojie
    School of Computer, Beijing University of Posts and Telecommunications
  • Zhong Yixin
    School of Computer, Beijing University of Posts and Telecommunications

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This paper presents a work of function labeling for unparsed Chinese text. Unlike other attempts that utilize the full parse trees, we propose an effective way to recognize function labels directly based on lexical information, which is easily scalable for languages that lack sufficient parsing resources. Furthermore, we investigate a general method to iteratively simplify a sentence, thus transferring complicated sentence into structurally simple pieces. By means of a sequence learning model with hidden Markov support vector machine, we achieve the best F-measure of 87.40 on the text from Penn Chinese Treebank resources - a statistically significant improvement over the existing Chinese function labeling systems.

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