词性标注中基于隐马尔可夫模型的命名实体识别

2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT) Pub Date : 2008-10-31 DOI:10.1109/ICADIWT.2008.4664380

R. Ageishi, T. Miura

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引用次数: 11

摘要

在本研究中，我们提出了如何将随机过程与基于规则的技术相结合来识别形态分析中的命名实体。本文初步探讨了隐马尔可夫模型(HMM)在英语文本标注中的应用，并将重点放在命名实体识别上。我们讨论了基于规则的n个连续词的规则提取方法，并通过一些实验结果证明了我们的方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Named entity recognition based on a Hidden Markov Model in part-of-speech tagging

In this investigation, we propose how to combine stochastis process with rule-based techniques to recognize named entity in morphological analysis. We discuss Hidden Markov Model (HMM) for tagging English texts tentatively and we focus our attention on named entity recognition. We discuss rule-based approach over n consecutive words for rule extraction and show the usefulness of our approach by some experimental results.

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来源期刊

2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)

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