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Named entity recognition in Assamese using CRFS and rules

机译:使用CRF和规则命名在assamese中的实体识别

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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.
机译:命名实体识别(ner)是所有自然语言处理(NLP)应用程序中的重要任务。它是将合适的名词识别和分类为个人,地点,组织和杂项等课程的过程。与英语和其他欧洲语言进行了实质性的工作,与印度语言相比,实现了更高的准确性。虽然印度语言是一个困难而挑战的任务,但遭受资源的稀缺,但这些工作已经开始出现。本文使用条件随机字段和基于规则的方法讨论了在assamese中的NER工作,这给出了90-95%的F-Measure精度。

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