利用维基百科先验知识进行中文命名实体识别

Jianfeng Li, Conghui Zhu, Sheng Li, T. Zhao, Dequan Zheng
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引用次数: 5

摘要

信息提取是自然语言处理研究中的一个重要课题。命名实体识别作为信息抽取的基本任务之一,其效果对后续的关系抽取等任务有很大的影响。而NER的一大难点在于未知词的识别。针对这一问题,研究了利用维基百科外部信息方法的方法。维基百科是近年来发展迅速的在线百科全书。2016年,中国参赛人数达到86万。利用维基百科作为外部知识,将为识别未知词提供大量有价值的信息。选取维基百科条目,并将其作为特征组合到NER的条件随机场模型中。实验研究表明,该方法可以显著提高NER的有效性。
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Exploiting Wikipedia priori knowledge for Chinese named entity recognition
Information Extraction is an important task in Natural Language Processing research. Named Entity Recognition as one of the basic tasks of information extraction, the effect has a great impact on the subsequent tasks such as Relation Extraction. And a major difficulty of NER lies in the unknown word identification. For this issue, method of exploiting Wikipedia external information methods was studied. Wikipedia is a rapid developing online encyclopedia in recent years. In 2016, the number of Chinese entries has reached 860,000. Huge valuable information will be provided to identify unknown words by Exploiting Wikipedia as external knowledge. The Wikipedia entries have been selected, and combined into the Conditional Random Field model of NER as features. The experimental studies demonstrate that this method can improve the effectiveness of NER significantly.
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