Named Entity Recognition using Hybrid Machine Learning Approach

摘要：

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.

关键词： Machine learning named entity recognition.

作者: Raymond Chiong Wang Wei

作者单位: School of Information Technology Swinburne University of Technology (Sarawak Campus) State Complex, 93576 Kuching Sarawak, Malaysia

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 578-583

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）