Title: Automatic Construction and Ranking of Topical Keyphrases on Collections of Short Documents
Abstract: Previous chapter Next chapter Full AccessProceedings Proceedings of the 2014 SIAM International Conference on Data Mining (SDM)Automatic Construction and Ranking of Topical Keyphrases on Collections of Short DocumentsMarina Danilevsky, Chi Wang, Nihit Desai, Xiang Ren, Jingyi Guo, and Jiawei HanMarina Danilevsky, Chi Wang, Nihit Desai, Xiang Ren, Jingyi Guo, and Jiawei Hanpp.398 - 406Chapter DOI:https://doi.org/10.1137/1.9781611973440.46PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We introduce a framework for topical keyphrase generation and ranking, based on the output of a topic model run on a collection of short documents. By shifting from the unigramcentric traditional methods of keyphrase extraction and ranking to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. Our method defines a function to rank topical keyphrases so that more highly ranked keyphrases are considered to be more representative phrases for that topic. We study the performance of our framework on multiple real world document collections, and also show that it is more scalable than comparable phrase-generating models. Previous chapter Next chapter RelatedDetails Published:2014eISBN:978-1-61197-344-0 https://doi.org/10.1137/1.9781611973440Book Series Name:ProceedingsBook Code:PRDT14Book Pages:1-1086
Publication Year: 2014
Publication Date: 2014-04-28
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 58
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot