Session topic mining for interactive text based on conversational content
Traditional theme mining model generally digs out the document theme from the interactive text only.In order to explore the session topic and improve the universality of mining model,a kind of interactive text session topic generation model based on the content of the dialogue was put forward.Firstl...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2016-09-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016238/ |
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| author | Jie PENG Yongge SHI Shengbao GAO |
| author_facet | Jie PENG Yongge SHI Shengbao GAO |
| author_sort | Jie PENG |
| collection | DOAJ |
| description | Traditional theme mining model generally digs out the document theme from the interactive text only.In order to explore the session topic and improve the universality of mining model,a kind of interactive text session topic generation model based on the content of the dialogue was put forward.Firstly,by analyzing the characteristics of interactive text and based on the concept of topic tree,a dialog spanning tree was defined with a five-layer structure.Based on this and LDA,the model of session topic generation(ST-LDA)was built.At last,Gibbs sampling method was adopted to deduce the ST-LDA and obtaining session topic and its distribution probability.The results show that the ST-LDA model can dig out a session topic effectively from the interactive text.Besides,the results can reduce the complexity of the classification algorithm and can be back to the theme—participants association.It also has a good universality. |
| format | Article |
| id | doaj-art-1f468f1f045943fbbac302ce1df4c291 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2016-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-1f468f1f045943fbbac302ce1df4c2912025-01-15T03:14:11ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012016-09-013213914559606825Session topic mining for interactive text based on conversational contentJie PENGYongge SHIShengbao GAOTraditional theme mining model generally digs out the document theme from the interactive text only.In order to explore the session topic and improve the universality of mining model,a kind of interactive text session topic generation model based on the content of the dialogue was put forward.Firstly,by analyzing the characteristics of interactive text and based on the concept of topic tree,a dialog spanning tree was defined with a five-layer structure.Based on this and LDA,the model of session topic generation(ST-LDA)was built.At last,Gibbs sampling method was adopted to deduce the ST-LDA and obtaining session topic and its distribution probability.The results show that the ST-LDA model can dig out a session topic effectively from the interactive text.Besides,the results can reduce the complexity of the classification algorithm and can be back to the theme—participants association.It also has a good universality.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016238/interactivetextconversationcontentsessiontopicminingdialogspanningtreelatentDirichletallocation |
| spellingShingle | Jie PENG Yongge SHI Shengbao GAO Session topic mining for interactive text based on conversational content Dianxin kexue interactivetext conversationcontent sessiontopicmining dialogspanningtree latentDirichletallocation |
| title | Session topic mining for interactive text based on conversational content |
| title_full | Session topic mining for interactive text based on conversational content |
| title_fullStr | Session topic mining for interactive text based on conversational content |
| title_full_unstemmed | Session topic mining for interactive text based on conversational content |
| title_short | Session topic mining for interactive text based on conversational content |
| title_sort | session topic mining for interactive text based on conversational content |
| topic | interactivetext conversationcontent sessiontopicmining dialogspanningtree latentDirichletallocation |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016238/ |
| work_keys_str_mv | AT jiepeng sessiontopicminingforinteractivetextbasedonconversationalcontent AT yonggeshi sessiontopicminingforinteractivetextbasedonconversationalcontent AT shengbaogao sessiontopicminingforinteractivetextbasedonconversationalcontent |