...
首页> 外文期刊>Indian Journal of Science and Technology >Randomized Kernel Approach for Named Entity Recognition in Tamil
【24h】

Randomized Kernel Approach for Named Entity Recognition in Tamil

机译:泰米尔语中命名实体识别的随机核方法

获取原文
           

摘要

In this paper, we present a new approach for Named Entity Recognition (NER) in Tamil language using Random Kitchen Sink algorithm. Named Entity recognition is the process of identification of Named Entities (NEs) from the text. It involves the identifying and classifying predefined categories such as person, location, organization etc. A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches. In this work, we implement the NER system for Tamil using Random Kitchen Sink algorithm which is a statistical and supervised approach. The NER system is also implemented using Support Vector Machine (SVM) and Conditional Random Field (CRF). The overall performance of the NER system was evaluated as 86.61% for RKS, 81.62% for SVM and 87.21% for CRF. Additional results have been taken in SVM and CRF by increasing the corpus size and the performance are evaluated as 86.06% and 87.20% respectively.
机译:在本文中,我们提出了一种使用随机厨房接收器算法的泰米尔语命名实体识别(NER)的新方法。命名实体识别是从文本中识别命名实体(NE)的过程。它涉及识别和分类预定义的类别,例如人员,位置,组织等。使用各种机器学习方法,在英语和印度语的命名实体识别领域已经完成了很多工作。在这项工作中,我们使用统计和监督方法的随机厨房水槽算法为泰米尔人实施NER系统。 NER系统还使用支持向量机(SVM)和条件随机场(CRF)来实现。 NER系统的整体性能评估为RKS为86.61%,SVM为81.62%,CRF为87.21%。通过增加语料库大小,在SVM和CRF中取得了其他结果,性能评估分别为86.06%和87.20%。

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