【摘要】 条件随机场(CRFs)是一种十分优秀的统计学习模型,文中尝试将其引入到文本分类之中,提出了一种基于CRFs的文本分类模型。首先通过特征选择将待分类文档和文档类别分别表示成为CRFs的观察序列和状态序列,然后使用文本分类相关领域知识定义特征函数来提取序列之间的关联特征,再采用前向或后向算法评估出给定观察序列条件下各状态序列的概率,据此实现待分类文档的分类。分析表明,这种新模型语义清晰,计算直观,易于融合各种文本分类领域知识,分类效率较高。更多还原

【Abstract】 Conditional random fields(CRFs) is an excellent statistical learning model.Importing it into text categorization,it proposes a text categorization model based on CRFs.Firstly,by choosing feature words,it describes a document as an observation sequence and each category as a state sequence.Then,using domain knowledges related to text categorization,it defines feature functions to extract association features between the sequences.Finally,it uses the forward or backward algorithm to find the probability of each state sequence in a given observation sequence,and uses these probabilities to categorize the document.The analysis shows that this new model has a good semantic interpretation,a strong ability to merge domain knowledge of text categorization,and a high efficiency to categorize documents.更多还原