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Application of association rules mining to Named Entity Recognition and co-reference resolution for the Indonesian language

机译:关联规则挖掘在印尼语实体的命名实体识别和共指解析中的应用

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摘要

In this paper, we propose a new method, association rules mining for Named Entity Recognition (NER) and co-reference resolution. The method uses several morphological and lexical features such as Pronoun Class (PC) and Name Class (NC), String Similarity (SP) and Position (P) in the text, into a vector of attributes. Applied to a corpus of newspaper in the Indonesian language, the method outperforms state-of-the-art maximum entropy method in name entity recognition and is comparable with state-of-the-art machine-learning methods, decision tree, for co-reference resolution.
机译:在本文中,我们提出了一种新方法,即用于命名实体识别(NER)和共引用解析的关联规则挖掘。该方法将文本中的代词类(PC)和名称类(NC),字符串相似度(SP)和位置(P)等几种形态和词汇特征用作属性向量。该方法应用于印尼语报纸语料库,在名称实体识别方面胜过了最新的最大熵方法,并且可以与最新的机器学习方法(决策树)相媲美。参考分辨率。

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