使用CRFS和规则在阿萨姆邦进行命名实体识别

Padmaja Sharma, U. Sharma, J. Kalita
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引用次数: 9

摘要

命名实体识别(NER)是所有自然语言处理(NLP)应用中的一个重要任务。它是对专有名词进行人、地、组织、杂等类的识别和分类的过程。用英语和其他欧洲语言进行了大量的工作,与印度语言相比,取得了更高的准确性。尽管印度语言的NER是一项困难和具有挑战性的任务,并且受到资源稀缺的影响,但这种工作最近开始出现。本文讨论了在阿萨姆邦使用条件随机场和基于规则的方法进行NER的工作,该方法给出了90-95%准确率的f度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Named entity recognition in Assamese using CRFS and rules
Named Entity Recognition (NER) is an important task in all Natural Language Processing (NLP) applications. It is the process of identifying and classifying the proper noun into classes such as person, location, organization and miscellaneous. Substantial work has been done in English and other European languages, achieving greater accuracy compared to the Indian Languages. Although NER in Indian languages is a difficult and challenging task and suffers from scarcity of resources, such work has started to appear recently. This paper discusses work on NER in Assamese using both Conditional Random Fields and a Rule-Based approach which gives an F-measure of 90-95% accuracy.
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