文章基本信息

标题：An Efficient Information Extraction Model for personal named entity
本地全文：下载
作者：Teena A.Sunny ; G. Naveen Sundar
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：3-3
出版社：Seventh Sense Research Group
摘要：Named entity recognition (NER) is one of the key techniques in language processing tasks such as information extraction. This paper focuses mainly on recognition of named entity using distance based clustering and attributes extraction patterns. The ultimate goal of the paper is to reduce ambiguity of person names with higher precision and recall and to avoid duplicity.
关键词：Unsupervised learning; precision; recall; Ambiguity; Bigrams; attribute extraction; clustering; tokens