Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8850
Title: Machine learning approaches for Chinese shallow parsers
Authors: Lu, Q 
Zhou, J
Xu, RF
Keywords: Grammars
Learning (artificial intelligence)
Natural languages
Issue Date: 2003
Publisher: IEEE
Source: 2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 4, p. 2309-2314 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/8850
ISBN: 0-7803-8131-9
DOI: 10.1109/ICMLC.2003.1259893
Appears in Collections:Conference Paper

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