NDLI: Named Entity Recognition Using Hybrid Machine Learning Approach

Content Provider	IEEE Xplore Digital Library
Author	Chiong, R. Wang Wei
Copyright Year	2006
Description	Author affiliation: Sch. of Inf. Technol., Swinburne Univ. of Technol., Sarawak (Chiong, R.; 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
Starting Page	578
Ending Page	583
File Size	7673562
Page Count	6
File Format	PDF
ISBN	1424404754
DOI	10.1109/COGINF.2006.365549
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Hidden Markov models Tagging Training data Humans Knowledge based systems Entropy Error correction Information technology Robustness named entity recognition
Content Type	Text
Resource Type	Article

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