TY - GEN
T1 - Machine learning approaches for chinese shallow parsers
AU - Lu, Qin
AU - Zhou, Jing
AU - Xu, Rui Feng
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
KW - Machine learning algorithms
KW - Natural language processing
KW - Shallow parsers Introduction
UR - https://www.scopus.com/pages/publications/1542315441
M3 - 会议稿件
AN - SCOPUS:1542315441
SN - 0780378652
SN - 9780780378650
T3 - International Conference on Machine Learning and Cybernetics
SP - 2309
EP - 2314
BT - 2003 International Conference on Machine Learning and Cybernetics, ICMLC 2003
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Machine Learning and Cybernetics, ICMLC 2003
Y2 - 2 November 2003 through 5 November 2003
ER -