PTR: Phrase-Based Topical Ranking for Automatic Keyphrase Extraction in Scientific Publications

Automatic keyphrase extraction plays an important role for many information retrieval (IR) and natural language processing (NLP) tasks. Motivated by the facts that phrases have more semantic information than single words and a document consists of multiple semantic topics, we present PTR, a phrase-b...

Full description

Saved in:
Bibliographic Details
Published inNeural Information Processing pp. 120 - 128
Main Authors Wang, Minmei, Zhao, Bo, Huang, Yihua
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Automatic keyphrase extraction
LDA
PageRank
Online AccessGet full text

Cover

More Information
Summary:Automatic keyphrase extraction plays an important role for many information retrieval (IR) and natural language processing (NLP) tasks. Motivated by the facts that phrases have more semantic information than single words and a document consists of multiple semantic topics, we present PTR, a phrase-based topical ranking method for keyphrase extraction in scientific publications. Candidate keyphrases are divided into different topics by LDA and used as vertices in a phrase-based graph of the topic. We then decompose PageRank into multiple weighted-PageRank to rank phrases for each topic. Keyphrases are finally generated by selecting candidates according to their overall scores on all related topics. Experimental results show that PTR has good performance on several datasets.
ISBN:9783319466804
3319466801
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-46681-1_15