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Title

Named entity recognition using hybrid machine learning approach

Author(s)

Chiong, Raymond;  Wang, Wei

Abstract

This paper presents a hybrid method using machine learning approach for named entity recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses maximum entropy model (MEM) and hidden Markov model (HMM) successively. We report on the performance of our proposed NER system using British National Corpus (BNC). In the recognition process, we first use MEM to identify the named entities in the corpus by imposing some temporary tagging as references. The MEM walkthrough can be regarded as a training process for HMM, as we then use HMM for the final tagging. We show that with enough training data and appropriate error correction mechanism, this approach can achieve higher precision and recall than using a single statistical model We conclude with our experimental results that indicate the flexibility of our system in different domains.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Sarawak Campus. School of Information Technology and Multimedia

Source

Proceedings 5th IEEE International Conference on Cognitive Informatics (ICCI 2006), Beijing, China, 17-19 July 2006, p. 578-583

Publication year

2006

Keyword(s)

Artificial intelligence;  British National Corpus;  Computational linguistics;  Hidden Markov models;  Learning;  Machine learning;  Maximum entropy methods;  Maximum entropy model;  Named entity recognition

Publisher

IEEE Computer Society

ISBN

1424404754

Publisher URL

http://dx.doi.org/10.1109/COGINF.2006.365549

Copyright

Copyright © 2006 IEEE. Published version of the paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Full text

Peer reviewed