Domain-Specific Keyphrase Extraction
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 668-672.
Abstract
Keyphrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very laborious. Therefore it is highly desirable to automate the keyphrase extraction process. This paper shows that a simple procedure for keyphrase extraction based on the naive Bayes learning scheme performs comparably to the state of the art. It goes on to explain how this procedure's performance can be boosted by automatically tailoring the extraction process to the particular document collection at hand. Results on a large collection of technical reports in computer science show that the quality of the extracted keyphrases improves significantly when domain-specific information is exploited.
@inproceedings{ijcai1999p95,
title = {Domain-Specific Keyphrase Extraction},
author = {Frank, Eibe and Paynter, Gordon W. and Witten, Ian H. and Gutwin, Carl and Nevill-Manning, Craig G.},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {668--672},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/95},
}
TY - CPAPER TI - Domain-Specific Keyphrase Extraction AU - Frank, Eibe AU - Paynter, Gordon W. AU - Witten, Ian H. AU - Gutwin, Carl AU - Nevill-Manning, Craig G. T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 668 EP - 672 UR - https://ijcai.org/proceedings/1999/95 ER -Copied
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