关键词: 命名实体识别 命名实体 特征选择 文本分类 隐马尔可夫模型

Abstract:

Text Classification （TC） is the process of automatically assigning predefined categories to free text documents, which is very important to informat   ion retrieval and some other areas. The most important step in TC is how to select the features that can effectively represent the class information of   the original documents. In some TC applications,documents usually contain lots of named entities, e.g., some organization names in specific areas, which  may significantly influence the classification of the documents. While in the recent researches, the selection of features mainly focuses on the orthog  raphy of words, disregarding the information the word contains as a named entity. To solve this problem, this paper proposes a method of feature selecti on for text classification based on named entity recognition （NER）. This method makes use of the category information of the word as a named entity, a  as well as the orthography characteristics. According to the experiments, this method improves the efficiency compared with the classic feature selectio n methods.

Key words:  （named entity recognition（NER）, named entity, feature selection, text classification, hidden m arkov model（HMM））