使用泰卢固语机器学习技术的命名实体识别

M. H. Khanam, Md.A. Khudhus, M. Babu
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引用次数: 12

摘要

在本文中,我们描述了混合方法,即基于规则的方法和机器学习技术的结合,即用于命名实体识别(NER)的条件随机场(CRF)。命名实体识别的主要目标是将文档中的所有命名实体(NE)分类为预定义的类,如人名、位置名称、组织名称。本文首先概述了基于规则方法的命名实体识别器。在该方法中,我们编制了人员、地点和组织名称的公报列表,一些后缀和前缀特征以及包含20万个单词的字典来识别名称实体的类别。此外,我们使用机器学习技术,即CRF来提高系统的准确性。
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Named Entity Recognition using Machine learning techniques for Telugu language
In this paper, we depict hybrid approach, i.e., combination of rule based approach and machine learning techniques, i.e Conditional Random Fields (CRF) for Named Entity Recognition (NER). The main objective of Named Entity Recognition is to categorize all Named Entities (NE) in a document into predefined classes like Person name, Location name, Organization name. This paper first outlines the Named Entity Recognizer using rule based approach. In this approach we prepared Gazette lists for names of persons, locations and organizations, some suffix and prefix features and dictionary consist of 200000 words to recognize the category of names entities. Further, we used Machine learning technique, i.e., CRF in order to improve the accuracy of the system.
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