首页> 外文会议>International conference on asian language processing >Named entity recognition in Assamese using CRFS and rules
【24h】

Named entity recognition in Assamese using CRFS and rules

机译:使用CRFS和规则在阿萨姆语中命名实体识别

获取原文

摘要

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:在所有自然语言处理(NLP)应用程序中,命名实体识别(NER)是一项重要任务。这是识别专有名词并将其分类为人,位置,组织和其他类别的过程。用英语和其他欧洲语言进行了大量工作,与印度语言相比,其准确性更高。尽管印度语言的NER是一项艰巨而具有挑战性的任务,并且资源匮乏,但这种工作最近才开始出现。本文讨论了使用条件随机场和基于规则的方法在Assamese中进行NER的工作,该方法给出了90-95%的准确度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号