Automated categorisation of e-journals by synonym analysis of n-grams

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Hussey, R., Williams, S. and Mitchell, R. (2011) Automated categorisation of e-journals by synonym analysis of n-grams. The International Journal on Advances in Software, 4 (3-4). pp. 532-542. ISSN 1942-2628

Abstract/Summary

Automatic keyword or keyphrase extraction is concerned with assigning keyphrases to documents based on words from within the document. Previous studies have shown that in a significant number of cases author-supplied keywords are not appropriate for the document to which they are attached. This can either be because they represent what the author believes a paper is about not what it actually is, or because they include keyphrases which are more classificatory than explanatory e.g., “University of Poppleton” instead of “Knowledge Discovery in Databases”. Thus, there is a need for a system that can generate an appropriate and diverse range of keyphrases that reflect the document. This paper proposes two possible solutions that examine the synonyms of words and phrases in the document to find the underlying themes, and presents these as appropriate keyphrases. Using three different freely available thesauri, the work undertaken examines two different methods of producing keywords and compares the outcomes across multiple strands in the timeline. The primary method explores taking n-grams of the source document phrases, and examining the synonyms of these, while the secondary considers grouping outputs by their synonyms. The experiments undertaken show the primary method produces good results and that the secondary method produces both good results and potential for future work. In addition, the different qualities of the thesauri are examined and it is concluded that the more entries in a thesaurus, the better it is likely to perform. The age of the thesaurus or the size of each entry does not correlate to performance.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/27961
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Automatic Tagging; Document Classification; Keyphrases; Keyword Extraction; Single Document; Synonyms; Thesaurus
Publisher	IARIA
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Date Deposited:	11 May 2012 10:02	Date item deposited into CentAUR
Last Modified:	23 Jun 2024 10:20	Date item last modified

References

R. Hussey, S. Williams, and R. Mitchell. 2011. “Keyphrase Extraction by Synonym Analysis of n-grams for E-Journal Classification”, eKNOW, Proceedings of The Third International Conference on Information, Process, and Knowledge Management, pp. 83-86. Gosier, Guadeloupe/France. http://www.thinkmind.org/index.php?view=article&articleid= eknow_2011_4_30_60053 [Last accessed: 23 January 2012] [2] P .M. Roget. 1911. “Roget’ s Thesaurus of English Words and Phrases (Index)”. http://www.gutenberg.org/etext/10681 [Last accessed: 23 January 2012] [3] E. Frank, G.W. Paynter, I.H. Witten, C. Gutwin, and C.G. Nevill-Manning. 1999. “Domain-Specific Keyphrase Extraction”, Proceedings 16th International Joint Conference on Artificial Intelligence, pp. 668–673. San Francisco, CA Morgan Kaufmann Publishers. [4] S.C. Sood, S.H. Owsley, K.J. Hammond, and L. Birnbaum. 2007. “TagAssist: Automatic Tag Suggestion for Blog Posts”. Northwestern University. Evanston, IL, USA. http://www.icwsm.org/papers/2--Sood-Owsley-Hammond- Birnbaum.pdf [Last accessed: 23 January 2012] [5] Technorati. 2006. “Technorati”. http://www.technorati.com [Last accessed: 23 January 2012] [6] J. Goldstein, M. Kantrowtiz, V. Mittal, and J. Carbonell. 1999. “Summarising Text Documents: Sentence Selection and Evaluation Metrics”, ACM, pp. 121–128. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA. [7] C. Li, J. Wen, and H. Li. 2003. “Text Classification Using Stochastic Keyword Generation”, Twentieth International Conference on Machine Learning (ICML), pp 464–471. Washington DC. https://www.aaai.org/Papers/ICML/2003/ICML03-062.pdf [Last accessed: 23 January 2012] [8] K. Barker and N. Cornacchia. 2000. “Using Noun Phrase Heads to Extract Document Keyphrases”, AI ’00: Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence. pp. 40–52). London. [9] J. Wermter and U. Hahn. 2005. “Paradigmatic Modifiability Statistics for the Extraction of Complex Multi- Word Terms”. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP) pp. 843–850. Vancouver Association for Computational Linguistics. [10] K. Frantziy, S. Ananiadou, and H. Mimaz. 2000. “Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method”, International Journal on Digital Libraries , 3 (2), pp. 117-132. [11] D. Maynard and S. Ananiadou. 2000. “TRUCKS: a model for automatic multi-word term recognition”. Journal of Natural Language Processing, 8 (1), pp. 101-125. [12] D. Maynard and S. Ananiadou. 1999. “Term extraction using a similarity-based approach”. Recent Advances in Computational Terminology, pp. 261–278. [13] A. Joshi and R. Motwani. 2006. “Keyword Generation for Search Engine Advertising”, IEEE International Conference on Data Mining, pp. 490–496. [14] S. Scott and S. Matwin. 1998. “Text Classification Using WordNet Hypernyms”, Proceedings of the Association for Computational Linguistics, pp. 38–44. [15] G.A. Miller, C. Fellbaum, R. Tengi, P. Wakefield, and H. Langone. 2005. “WordNet”. Princeton University. http://WordNet.princeton.edu [Last accessed: 23 January 2012] [16] D. Greenhaus. 2002. “DigiTrad - Digital Tradition Folk Song Server”. http://www.mudcat.org/download.cfm [Last accessed: 23 January 2012] [17] Reuters. 1987. “Reuters-21578 Text Categorisation Collection”. http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.ht ml [Last accessed: 23 January 2012] [18] B. Wei, C. Zhang, and M. Ogihara. 2007. “Keyword Generation for Lyrics”, Austrian Computer Society (OCG). Comp. Sci. Dept., U. Rochester, USA. http://ismir2007.ismir.net/proceedings/ISMIR2007_p121_wei .pdf [Last accessed: 23 January 2012] [19] G. Ward. 2000. “Moby Project - Thesaurus”. http://icon.shef.ac.uk/Moby/mthes.html [Last accessed: 11 July 2011] [20] Academics Conferences International. 2009. “ACI E- Journals”. http://academic-conferences.org/ejournals.htm [Last accessed: 23 January 2012] [21] M.F. Porter. 1980. “An algorithm for suffix stripping”, Program, 14(3) pp. 130–137. [22] M.L. McHale. 1998. “A Comparison of WordNet and Roget's Taxonomy for Measuring Semantic Similarity”,. http://acl.ldc.upenn.edu/W/W98/W98-0716.pdf accessed: 23 January 2012]

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