Abstract
The ability to discover the topic of a large set of text documents using relevant keyphrases is usually regarded as a very tedious task if done by hand. Automatic keyphrase extraction from multi-document data sets or text clusters provides a very compact summary of the contents of the clusters, which often helps in locating information easily. We introduce an algorithm for topic discovery using keyphrase extraction from multi-document sets and clusters based on frequent and significant shared phrases between documents. The keyphrases extracted by the algorithm are highly accurate and fit the cluster topic. The algorithm is independent of the domain of the documents. Subjective as well as quantitative evaluation show that the algorithm outperforms keyword-based cluster-labeling algorithms, and is capable of accurately discovering the topic, and often ranking it in the top one or two extracted keyphrases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Turney, P.D.: Learning algorithms for keyphrase extraction. Information Retrieval 2, 303–336 (2000)
Frank, E., Paynter, G., Witten, I., Gutwin, C., Nevill-Manning, C.: Domain-specific keyphrase extraction. In: 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), Stockholm, Sweden, pp. 668–673 (1999)
Hammouda, K., Kamel, M.: Efficient phrase-based document indexing for web document clustering. IEEE Transactions on Knowledge and Data Engineering 16, 1279–1296 (2004)
Neto, J., Santos, A., Kaestner, C., Freitas, A.: Document clustering and text summarization. In: Proc. 4th International Conference Practical Applications of Knowledge Discovery and Data Mining (PADD 2000), London, UK, pp. 41–55 (2000)
Bakus, J., Kamel, M., Carey, T.: Extraction of text phrases using hierarchical grammar. In: Cohen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2338, pp. 319–324. Springer, Heidelberg (2002)
Mani, I., Bloedorn, E.: Multi-document summarization by graph search and matching. In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI 1997), Providence, RI, pp. 622–628 (1997)
Clifton, C., Cooley, R., Rennie, J.: TopCat: data mining for topic identification in a text corpus. IEEE Transactions on Knowledge and Data Engineering 16, 949–964 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hammouda, K.M., Matute, D.N., Kamel, M.S. (2005). CorePhrase: Keyphrase Extraction for Document Clustering. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_26
Download citation
DOI: https://doi.org/10.1007/11510888_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
eBook Packages: Computer ScienceComputer Science (R0)