An artificial immune network approach for pinyin-to- character conversion

Authors:
Wei Jiang;Xiu-Li Pang
Affiliations:
Research Center of Information Management and Information System, Harbin Institute of Technology, Harbin, P.R.China;School of Economics and Business Administration, Hei Long Jiang University and School of Management, Harbin Institute of Technology, Harbin, P.R.China
Venue:
VECIMS'09 Proceedings of the 2009 IEEE international conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems
Year:
2009

Citing 7
Cited 0

A Cache-Based Natural Language Model for Speech Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Toward a unified approach to statistical language modeling for Chinese

ACM Transactions on Asian Language Information Processing (TALIP)
Probabilistic top-down parsing and language modeling

Computational Linguistics
A new statistical approach to Chinese Pinyin input

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Mining Pinyin-to-character conversion rules from large-scale corpus: a rough set approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

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.