Abstract
Nowadays, a large amount of text documents are produced on a daily basis, so we need efficient and effective access to their content. News articles, blogs and technical reports are often lengthy, so the reader needs a quick overview of the underlying content. To that end we present graph-based models for keyword extraction, in order to compare the Bag of Words model with the Graph of Words model in the keyword extraction problem. We compare their performance in two publicly available datasets using the evaluation measures Precision@10, mean Average Precision and Jaccard coefficient. The methods we have selected for comparison are grouped into two main categories. On the one hand, centrality measures on the formulated Graph-of-Words (GoW) are able to rank all words in a document from the most central to the less central, according to their score in the GoW representation. On the other hand, community detection algorithms on the GoW provide the largest community that contains the key nodes (words) in the GoW. We selected these methods as the most prominent methods to identify central nodes in a GoW model. We conclude that term-frequency scores (BoW model) are useful only in the case of less structured text, while in more structured text documents, the order of words plays a key role and graph-based models are superior to the term-frequency scores per document.
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References
Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: An overview of graph-based keyword extraction methods and approaches. J. Inf. Organ. Sci. 39(1), 1–20 (2015)
Abilhoa, W.D., De Castro, L.N.: A keyword extraction method from twitter messages represented as graphs. Appl. Math. Comput. 240, 308–325 (2014)
Lahiri, S., Choudhury, S.R., Caragea, C.: Keyword and keyphrase extraction using centrality measures on collocation networks. arXiv preprint arXiv:1401.6571 (2014)
Boudin, F.: A comparison of centrality measures for graph-based keyphrase extraction. In: International Joint Conference on Natural Language Processing (IJCNLP), pp. 834–838 (2013)
Tsatsaronis, G., Varlamis, I., Nørvåg, K.: Semanticrank: ranking keywords and sentences using semantic graphs. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 1074–1082. Association for Computational Linguistics (2010)
Xie, Z.: Centrality measures in text mining: prediction of noun phrases that appear in abstracts. In: Proceedings of the ACL Student Research Workshop, pp. 103–108. Association for Computational Linguistics (2005)
Grineva, M., Grinev, M., Lizorkin, D.: Extracting key terms from noisy and multitheme documents. In: Proceedings of the 18th international conference on World wide web, pp. 661–670. ACM (2009)
Rousseau, F., Vazirgiannis, M.: Graph-of-word and TW-IDF: new approach to ad hoc IR. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 59–68. ACM (2013)
Nie, T., Guo, Z., Zhao, K., Lu, Z.M.: Using mapping entropy to identify node centrality in complex networks. Phys. A 453, 290–297 (2016)
Gialampoukidis, I., Kalpakis, G., Tsikrika, T., Vrochidis, S., Kompatsiaris, I.: Key player identification in terrorism-related social media networks using centrality measures. In: European Intelligence and Security Informatics Conference (EISIC 2016), pp. 17–19. August (2016)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Gr. Algorithms Appl. 10(2), 191–218 (2006)
Acknowledgements
This work was supported by the projects H2020-645012 (KRISTINA) and H2020-700024 (TENSOR), funded by the European Commission.
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Batziou, E., Gialampoukidis, I., Vrochidis, S., Antoniou, I., Kompatsiaris, I. (2017). Unsupervised Keyword Extraction Using the GoW Model and Centrality Scores. In: Kompatsiaris, I., et al. Internet Science. INSCI 2017. Lecture Notes in Computer Science(), vol 10673. Springer, Cham. https://doi.org/10.1007/978-3-319-70284-1_26
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DOI: https://doi.org/10.1007/978-3-319-70284-1_26
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