Machine learning approaches for chinese shallow parsers

Qin Lu, Jing Zhou, Rui Feng Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


In this paper, we present two machine-learning algorithms, namely, transformation-based error-driven learning (TEL) and memory-based learning (MBL) to improve the performance of a Chinese shallow parser. The Algorithm not only can handle nested chunking data, but also different phrase types (e.g. NP, VP, S etc.). Results show that TEL can achieve better recall rate, yet MBL is less sensitive to nesting and requires much less computation.
Original languageEnglish
Title of host publicationInternational Conference on Machine Learning and Cybernetics
Number of pages6
Publication statusPublished - 1 Dec 2003
Event2003 International Conference on Machine Learning and Cybernetics - Xi'an, China
Duration: 2 Nov 20035 Nov 2003


Conference2003 International Conference on Machine Learning and Cybernetics


  • Machine learning algorithms
  • Natural language processing
  • Shallow parsers Introduction

ASJC Scopus subject areas

  • Artificial Intelligence

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