---
title: "Domain-Specific Keyphrase Extraction"
authors:
  - "Eibe Frank"
  - "Gordon W. Paynter"
  - "Ian H. Witten"
  - "Carl Gutwin"
  - "Craig G. Nevill-Manning"
doi: ""
url: "https://ijcai.org/proceedings/1999/95"
pdf: "https://ijcai.org/proceedings/1999/0095.pdf"
venue: "Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence"
year: 1999
pages: "668-672"
track: "Machine Learning"
keywords:
---

# Domain-Specific Keyphrase Extraction

Eibe Frank, Gordon W. Paynter, Ian H. Witten, Carl Gutwin, Craig G. Nevill-Manning

*Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence*, 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.