基于隐马尔可夫模型的主观句识别  

Subjective Sentence Recognition Based on Hidden Markov Model

作　　者：刘培玉[1,2] 荀静[2] 费绍栋[2] 朱振方[3] LIU Peiyu XUN Jing FEI Shaodong ZHU Zhenfanga(School of Information Engineering, Shandong Yingcai University, Jinan, Shandong 250104, China School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong 250014, China School of Information Science and Electric Engineering, Shandong Jiaotong University, Jinan, Shandong 250357, China)

机构地区：[1]山东英才学院信息工程学院,山东济南250104 [2]山东师范大学信息科学与工程学院,山东济南250014 [3]山东交通学院信息科学与电气工程学院,山东济南250357

出　　处：《中文信息学报》2016年第4期206-212,共7页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61373148);国家社会科学基金(12BXW040);山东省自然科学基金(ZR2012FM038;ZR2011FM030);山东省优秀中青年科学家奖励基金(BS2013DX033)

摘　　要：文本情感倾向分析是意见挖掘和情感文摘中的一个重要环节,而在情感倾向分析中涉及到的是主观性文本,这就需要进行主客观文本分类。当前的主客观文本分类方法主要是基于特征词典的概率统计方法,并没有考虑特征之间的语法与语义关系。针对该问题,该文提出一种基于隐马尔可夫模型(HMM)的主观句识别方法。该方法首先从训练语料中抽取具有明显分类效果的七类主客观特征,然后每个句子应用HMM进行特征角色类别标注,并依据标注的结果计算句子的权重,最终识别主观句。该方法在第六届中文倾向性分析评测任务中能够有效地识别主观句。The current subjective and objective text classification methods are mainly based on statistical model over the feature lexicon,which didn＇t take into account the syntax and semantic relationships between features.The paper proposes a Chinese subjective sentence recognition based on Hidden Markov Model.In this method,seven kinds of subjective and objective features for classification are extracted tagged among each sentence by HMM.The subjective sentences are decided by the importance of features and syntactic structure of sentences.The method is examined in the task of COAE2014 for its effeiciency.

关 键 词：隐马尔可夫模型 特征标注 主观句识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]