基于词性特征的明喻识别及要素抽取方法  

Part-of-Speech Based Simile Recognition and Component Extraction

作　　者：赵琳玲 王素格[1,2] 陈鑫[1] 王典 张兆滨 ZHAO Linling;WANG Suge;CHEN Xin;WANG Dian;ZHANG Zhaobin(School of Computer Science and Technology,Shanxi University,Taiyuan,Shanxi 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan,Shanxi 030006,China)

机构地区：[1]山西大学计算机与信息技术学院,山西太原030006 [2]山西大学计算智能与中文信息处理教育部重点实验室,山西太原030006

出　　处：《中文信息学报》2021年第1期81-87,95,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金(62076158);国家重点研发计划(2018YFB1005103);山西省重点研发计划(201803D421024)。

摘　　要：比喻是一种利用事物之间的相似点建立关系的修辞方式。明喻是比喻中最常见的形式,具有明显的喻词,例如"像",用于关联本体和喻体。近年来高考语文散文类鉴赏题中多有考查明喻句的试题,为了解答此类鉴赏题,需要识别比喻句中的本体和喻体要素。该文提出了基于词性特征的明喻识别及要素抽取方法。首先将句子中词向量化表示与词性特征向量化表示进行融合,将融合后的向量输入到BiLSTM中进行训练,然后利用CRF解码出全局最优标注序列;最后得到明喻识别和要素抽取的结果。公开数据集上的实验结果表明,该方法优于已有的单任务方法;同时也将该文方法应用于北京高考语文鉴赏题中比喻句的识别与要素抽取,验证了方法的可行性。Simile is the most common form in the metaphor, including obvious comparators, such as "like", used to relate tenor and vehicle. To better resolve the Chinese prose reading comprehension of the College Entrance Examination, this paper designs a method for the simile recognition and component extraction based on part-of-speech features. Firstly, the vector representation of the words in the sentence is merged with the representation of the part-of-speech. Then, the fused vector is input into BiLSTM model and the global optimal annotation sequence is decoded by CRF. Finally, the smile recognition and component extraction are generated according to annotated sequence. The experiment results show that the proposed method is better than the existing single task method on the open dataset.

关 键 词：比喻 本体 喻体 BiLSTM CRF 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]