NDLI: An Artificial Immune Network approach for Pinyin-to-character conversion

Content Provider	IEEE Xplore Digital Library
Author	Wei Jiang Xiu-Li Pang
Copyright Year	2009
Description	Author affiliation: Research Center of Information Management and Information System, Harbin Institute of Technology, China (Wei Jiang) \|\| School of Economics and Business Administration, Hei Long Jiang University, China (Xiu-Li Pang)
Abstract	This paper proposes a novel approach based on Artificial Immune Network for dealing with the task of Pinyin-to-character (PTC) conversion. The researches in recent years have nearly indicated that the sparse data problem and the independent identical distribution (iid.) assumption are two main difficulties of improving the PTC performance, and these two problems widely exist in the supervised learning methods. This paper presents an online learning approach to overcome the above problems. This model has a kind of ability of adaptively adjustment by using the feedback information, and in this model, the discriminative function gives the partial ordering relation of each immune chain so as to implement the partial perception online learning. The experiments show that our PTC conversion method based on the online learning technology can achieve a better performance than the n-gram language model, and this kind of improvement is hardly acquired by the classical supervised learning methods.
Starting Page	27
Ending Page	32
File Size	536526
Page Count	6
File Format	PDF
ISBN	9781424438082
ISSN	19449410
DOI	10.1109/VECIMS.2009.5068860
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Immune system Supervised learning Feedback Speech recognition Natural languages Error analysis Virtual environment Anthropometry Information management Management information systems Pinyin-to-character conversion Artificial Immune Network data mining
Content Type	Text
Resource Type	Article

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