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Exploiting Wikipedia priori knowledge for Chinese named entity recognition

机译:利用Wikipedia先验知识进行中文命名实体识别

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摘要

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.
机译:信息提取是自然语言处理研究的重要任务。将实体识别作为信息提取的基本任务之一,其效果对诸如关系提取之类的后续任务有很大的影响。 NER的主要困难在于未知单词的识别。针对此问题,研究了利用Wikipedia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。通过利用Wikipedia作为外部知识,将提供大量有价值的信息来识别未知单词。已选择Wikipedia条目,并将其合并为NER的条件随机字段模型作为特征。实验研究表明,该方法可以显着提高NER的有效性。

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