KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
Machine Learning
2013-06-04 v1 Information Retrieval
Abstract
We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.
Keywords
Cite
@article{arxiv.1306.0271,
title = {KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles},
author = {Marina Danilevsky and Chi Wang and Nihit Desai and Jingyi Guo and Jiawei Han},
journal= {arXiv preprint arXiv:1306.0271},
year = {2013}
}
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9 pages