标题:An intention-topic model based on verbs clustering and short texts topic mining
作者:Lu, Tingting ;Hou, Shifeng ;Chen, Zhenxiang ;Cui, Lizhen ;Zhang, Lei
通讯作者:Chen, Zhenxiang
作者机构:[Lu, Tingting ;Chen, Zhenxiang ;Zhang, Lei ] School of Information Science and Engineering, University of Jinan, Jinan, China;[Hou, Shifeng ] Library 更多
会议名称:15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
会议日期:26 October 2015 through 28 October 2015
来源:Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
出版年:2015
页码:837-842
DOI:10.1109/CIT/IUCC/DASC/PICOM.2015.124
摘要:Microblog, Twitter, status messages, the classified information website and so on are experiencing explosive growth with the development of web2.0, people prefer to use short texts to express their intentions and activities. Yet, when people submit some requirements through short texts, they hope to get a feedback which can help them to solve their problems rather than relevant content. Sometimes people need corresponding intention rather than similar content. However, current researches cannot solve the problem well. In this paper, we propose an intentiontopic model: Verb-Biterm Topic Model(V-BTM), which aims at corresponding intention matching.Intention is expressed by verbs and topic is expressed by BTM.Intention is the action of people want to express and topic is the goal of the intention. The key of the model is that people tend to express their intention with verbs and tend to express the topic with non-verb. In this model, firstly, we distinguish intentions with the verb clustering with the help of word2vec which is a deep learning tool. Secondly, we mine the topic using Biterm Topic Model(BTM) on the data without verbs. We carry out experiments on real-world short text collections. The results demonstrate that our approach can get better verb clustering and mine more coherent topics. Furthermore, the new model can be the base of our future researches. © 2015 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964270358&doi=10.1109%2fCIT%2fIUCC%2fDASC%2fPICOM.2015.124&partnerID=40&md5=d5448aaa0385d90a10ad9893b12264a7
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