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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/5882
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- Named entity recognition using hybrid machine learning approach
- Chiong, Raymond; Wang, Wei
- 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
- [Proceedings] 5th IEEE International Conference on Cognitive Informatics (ICCI 2006), Beijing, China, 17-19 July 2006, p. 578-583
- Publication year
- IEEE Computer Society
- Publisher URL
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