小说人物的分布表示及其应用研究  

Distributed Representation of Fictional Characters and Its Applications

作　　者：贾玉祥[1,2] 王璐 刘鹏程[1] 王钤 张岳 昝红英 JIA Yuxiang;WANG Lu;LIU Pengcheng;WANG Qian;ZHANG Yue;ZAN Hongying(School of Information Engineering,Zhengzhou University,Zhengzhou,Henan 450001,China;Collaborative Innovation Center of IoT Industrialization and Intelligent Production,Minjiang University,Fuzhou,Fujian 350108,China;School of Foreign Languages,Zhengzhou University,Zhengzhou,Henan 450001,China;School of Engineering,Westlake University,Hangzhou,Zhejing 310024,China)

机构地区：[1]郑州大学信息工程学院,河南郑州450001 [2]闽江学院物联网产业化与智能生产协同创新中心,福建福州350108 [3]郑州大学外语学院,河南郑州450001 [4]西湖大学工学院,浙江杭州310024

出　　处：《中文信息学报》2020年第12期92-99,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61402419);国家社会科学基金(14BYY096);闽江学院物联网产业化与智能生产协同创新中心开放基金(IIC1707);河南省科技攻关项目(192102210260);国家社会科学基金重大项目(18ZDA295)。

摘　　要：小说是以刻画人物为中心,通过完整的故事情节和具体的环境描写反映社会生活的一种文学体裁。对小说人物进行建模,是小说文本理解和小说文本挖掘的基础性工作。该文构建了大规模的小说语料库,抽取人物及其依存特征,提出基于skip-gram的人物向量训练方法,以人物为目标,以依存特征为上下文,基于训练出的人物向量,探索了小说人物相似度计算、小说人物聚类分析及小说人物画像等应用。实验结果表明,小说人物的分布表示有较好的应用效果。Novel is a literary genre that centers on character creation,depicting social life through complete plots and specific environmental descriptions.Modeling fictional characters is essential for literary text understanding and literary text mining.In this paper,we construct a large-scale novel corpus and extract characters and their dependency features.We propose a skip-gram based model to train character embeddings,with the character as the target while the dependency features as the contexts.Based on the trained character embeddings,we further investigate the tasks of character similarity computation,character clustering,and character profiling.The experimental results show a good performance of the distributed representation of fictional characters in the above tasks.

关 键 词：小说人物 分布表示 人物向量 人物聚类 人物画像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]