Abstract
In this paper, we present a combinational approach to automatically supplying keyphrases for a Chinese news documen. In particular, we discuss some factors that have an effect on forming an initial set of keyphrase candidates and filtering unimportant candidates out from the initial set, as well as selecting the best items from the set of the remaining candidates. Experiments show that the approach reaches a satisfactory result.
This work is partially funded by National Natural Science Foundation of Chinese (grant No. 60173005).
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References
Chien, L.F.: PAT-Tree-based keyword Extraction for Chinese Information Retrieval. In: Proceedings of the ACM SIGIR International Conference on Information Retrieval, pp. 50–59 (1997)
Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domainspecific keyphrase extraction. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 668–673. Morgan Kaufmann, California (1999)
Ong, T., Chen, H.: Updateable PAT-Tree Approach to Chinese Key Phrase Extraction using Mutual Information: A Linguistic Foundation for Knowledge Management. In: Proceedings of 2nd Asian Digital Labrary Conference, Taipei, Taiwan, November 8-9, pp. 63–84 (1999)
Turney, P.D.: Learning algorithms for keyphrase extraction. Information Retrieval 2, 303–336 (2000)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: Practical automatic keyphrase extraction. In: Proceedings of Digital Libraries 1999 (DL 1999), pp. 254–256. ACM Press, New York (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, H., Li, S., Yu, S., Kang, B.K. (2004). A Combining Approach to Automatic Keyphrases Indexing for Chinese News Documents. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_54
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DOI: https://doi.org/10.1007/978-3-540-24630-5_54
Publisher Name: Springer, Berlin, Heidelberg
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