哪个最重要?比较主题模型中概念和文档关系的影响

First Workshop on Insights from Negative Results in NLP Pub Date : 2020-11-01 DOI:10.18653/v1/2020.insights-1.5

Silvia Terragni, Debora Nozza, E. Fersini, M. Enza

{"title":"哪个最重要?比较主题模型中概念和文档关系的影响","authors":"Silvia Terragni, Debora Nozza, E. Fersini, M. Enza","doi":"10.18653/v1/2020.insights-1.5","DOIUrl":null,"url":null,"abstract":"Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.","PeriodicalId":441528,"journal":{"name":"First Workshop on Insights from Negative Results in NLP","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models\",\"authors\":\"Silvia Terragni, Debora Nozza, E. Fersini, M. Enza\",\"doi\":\"10.18653/v1/2020.insights-1.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.\",\"PeriodicalId\":441528,\"journal\":{\"name\":\"First Workshop on Insights from Negative Results in NLP\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Workshop on Insights from Negative Results in NLP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2020.insights-1.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Workshop on Insights from Negative Results in NLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.insights-1.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

摘要

主题模型已被广泛用于发现文档集合中的隐藏主题。在本文中，我们建议探讨两种不同类型的关系信息，即文档关系和概念关系的作用。虽然利用文献网络可以显著提高主题连贯性，但概念及其关系的引入并不会在数量和质量上影响结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

First Workshop on Insights from Negative Results in NLP

自引率

0.00%

发文量