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A co-occurrence based approach of automatic keyword expansion using mass diffusion

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Abstract

The performance of keyword expansion in prior methods is often enhanced by adopting external knowledge. Given a set of initial keywords, this paper is motivated to propose a novel method to expand semantically or conceptually related keywords from domain corpus by employing mass diffusion. A bipartite word network is thus constructed based on co-occurrence relations between initial keywords and candidate words. The expanded keywords are identified via two-step mass diffusion which is carried out in the bipartite network. Experimental results prove that the proposed method outperforms both the typical statistical-based approach and graph-based approach. Our research is expected to complement the theoretical framework of keyword expansion and is applicable to the scenarios of query expansion, thesaurus construction, and text clustering.

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References

  • Abilhoa, W. D., & De Castro, L. N. (2014a). A keyword extraction method from twitter messages represented as graphs. Applied Mathematics and Computation,240, 308–325.

    Google Scholar 

  • Abilhoa, W. D., & De Castro, L. N. (2014b). TKG: A graph-based approach to extract keywords from tweets. In Distributed computing and artificial intelligence, 11th International Conference (pp. 425–432). Cham: Springer.

  • Azad, H. K., & Deepak, A. (2019). Query expansion techniques for information retrieval: A survey. Information Processing and Management,56(5), 1698–1735.

    Google Scholar 

  • Beliga, S., Meštrović, A., & Martinčić-Ipšić, S. (2015). An overview of graph-based keyword extraction methods and approaches. Journal of information and organizational sciences,39(1), 1–20.

    Google Scholar 

  • Biswas, S. K., Bordoloi, M., & Shreya, J. (2018). A graph based keyword extraction model using collective node weight. Expert Systems with Applications,97, 51–59.

    Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer networks and ISDN systems,30(1–7), 107–117.

    Google Scholar 

  • Campos, R., Mangaravite, V., Pasquali, A., Jorge, A. M., Nunes, C., & Jatowt, A. (2018). A text feature based automatic keyword extraction method for single documents. In European conference on information retrieval (pp. 684–691). Cham: Springer.

  • Cava, W. (2011). U.S. Patent No. 7,962,463. Washington, DC: U.S. Patent and Trademark Office.

  • Chen, Y. H., Lu, E. J. L., & Tsai, M. F. (2014). Finding keywords in blogs: Efficient keyword extraction in blog mining via user behaviors. Expert Systems with Applications,41(2), 663–670.

    Google Scholar 

  • Chua, T. S., Neo, S. Y., Li, K. Y., Wang, G., Shi, R., Zhao, M, (2004). TRECVID 2004 search and feature extraction task by NUS PRIS. In NIST TRECVID workshop.

  • Das, D., & Petrov, S. (2011). Unsupervised part-of-speech tagging with bilingual graph-based projections. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (Vol. 1, pp. 600-609). Association for Computational Linguistics.

  • Duari, S., & Bhatnagar, V. (2019). sCAKE: Semantic connectivity aware keyword extraction. Information Sciences,477, 100–117.

    Google Scholar 

  • Ercan, G., & Cicekli, I. (2007). Using lexical chains for keyword extraction. Information Processing and Management,43(6), 1705–1714.

    Google Scholar 

  • Florescu, C., & Caragea, C. (2017). A position-biased pagerank algorithm for keyphrase extraction. In Thirty-first AAAI conference on artificial intelligence.

  • Gaglio, S., Re, G. L., & Morana, M. (2016). A framework for real-time Twitter data analysis. Computer Communications,73, 236–242.

    Google Scholar 

  • Hadzic, M., & Chang, E. (2005). Ontology-based support for human disease study. In Proceedings of the 38th Annual Hawaii international conference on system sciences (pp. 143a–143a). IEEE.

  • Hassan, H., & Menezes, A. (2013). Social text normalization using contextual graph random walks. In Proceedings of the 51st annual meeting of the association for computational linguistics (Volume 1: Long Papers) (Vol. 1, pp. 1577–1586).

  • Hughes, T., & Ramage, D. (2007). Lexical semantic relatedness with random graph walks. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL).

  • Hyung, Z., Park, J. S., & Lee, K. (2017). Utilizing context-relevant keywords extracted from a large collection of user-generated documents for music discovery. Information Processing and Management,53(5), 1185–1200.

    Google Scholar 

  • Kim, H. J., Lee, S., Lee, B., & Kang, S. (2010). Building concept network-based user profile for personalized web search. In 2010 IEEE/ACIS 9th international conference on computer and information science (pp. 567–572). IEEE.

  • Kim, S. N., Medelyan, O., Kan, M. Y., & Baldwin, T. (2010). Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles. In Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 21–26).

  • Lambiotte, R., Delvenne, J. C., & Barahona, M. (2014). Random walks, Markov processes and the multiscale modular organization of complex networks. IEEE Transactions on Network Science and Engineering,1(2), 76–90.

    MathSciNet  Google Scholar 

  • Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning (pp. 1188–1196).

  • Li, S., Sun, Y., & Soergel, D. (2015). A new method for automatically constructing domain-oriented term taxonomy based on weighted word co-occurrence analysis. Scientometrics,103(3), 1023–1042.

    Google Scholar 

  • Litvak, M., & Last, M. (2008). Graph-based keyword extraction for single-document summarization. In Proceedings of the workshop on multi-source multilingual information extraction and summarization (pp. 17–24). Association for Computational Linguistics.

  • Liu, J. G., Zhou, T., & Guo, Q. (2011). Information filtering via biased heat conduction. Physical Review E,84(3), 037101.

    Google Scholar 

  • Ma, S. P., Li, C. H., Tsai, Y. Y., & Lan, C. W. (2013). Web service discovery using lexical and semantic query expansion. In 2013 IEEE 10th International Conference on e-Business Engineering (pp. 423–428). IEEE.

  • Matsuo, Y., & Ishizuka, M. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools,13(01), 157–169.

    Google Scholar 

  • Mihalcea, R., & Tarau, P. (2004). Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing (pp. 404–411).

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119).

  • Mohsen, G., Al-Ayyoub, M., Hmeidi, I., & Al-Aiad, A. (2018). On the automatic construction of an Arabic thesaurus. In 2018 9th international conference on information and communication systems (ICICS) (pp. 243–247). IEEE.

  • Nasar, Z., Jaffry, S. W., & Malik, M. K. (2019). Textual keyword extraction and summarization: State-of-the-art. Information Processing and Management,56(6), 102088.

    Google Scholar 

  • Nasir, J. A., Varlamis, I., & Ishfaq, S. (2019). A knowledge-based semantic framework for query expansion. Information Processing and Management,56(5), 1605–1617.

    Google Scholar 

  • Nowroozi, M., Mirzabeigi, M., & Sotudeh, H. (2018). Constructing an ontology based on a thesaurus: A case of ASIS&TOnto based on the ASIS&T Web-based thesaurus. The Electronic Library,36(4), 750–764.

    Google Scholar 

  • Paliwal, A. V., Shafiq, B., Vaidya, J., Xiong, H., & Adam, N. (2012). Semantics-based automated service discovery. IEEE Transactions on Services Computing,5(2), 260–275.

    Google Scholar 

  • Papagiannopoulou, E., & Tsoumakas, G. (2018). Local word vectors guiding keyphrase extraction. Information Processing and Management,54(6), 888–902.

    Google Scholar 

  • Papagiannopoulou, E., & Tsoumakas, G. (2019). A review of keyphrase extraction (p. e1339). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.

    Google Scholar 

  • Peat, H. J., & Willett, P. (1991). The limitations of term co-occurrence data for query expansion in document retrieval systems. Journal of the american society for information science,42(5), 378–383.

    Google Scholar 

  • Shamim Khan, M., & Khor, S. (2004). Enhanced web document retrieval using automatic query expansion. Journal of the American Society for Information Science and Technology,55(1), 29–40.

    Google Scholar 

  • Siddiqi, S., & Sharan, A. (2015). Keyword and keyphrase extraction techniques: A literature review. International Journal of Computer Applications, 109(2), 18–23.

  • Vega-Oliveros, D. A., Gomes, P. S., Milios, E. E., & Berton, L. (2019). A multi-centrality index for graph-based keyword extraction. Information Processing and Management,56(6), 102063.

    Google Scholar 

  • Wang, J., Zhou, Y., Li, L., Hu, B., & Hu, X. (2009). Improving short text clustering performance with keyword expansion. In The sixth international symposium on neural networks (ISNN 2009) (pp. 291–298). Berlin: Springer.

  • Witten, I. H., Paynter, G. W., Frank, E., Gutwin, C., & Nevill-Manning, C. G. (2005). Kea: Practical automated keyphrase extraction. In Design and usability of digital libraries: Case studies in the Asia Pacific (pp. 129–152). IGI global.

  • Won, M., Martins, B., & Raimundo, F. (2019). Automatic extraction of relevant keyphrases for the study of issue competition. In Proceedings of the 20th international conference on computational linguistics and intelligent text processing, Berkeley, La Rochelle, France, April 7–13, 2019.

  • Wu, Y. (2018). Enriching a thesaurus as a better question-answering tool and information retrieval aid. Journal of Information Science,44(4), 512–525.

    Google Scholar 

  • Yang, K., Chen, Z., Cai, Y., Huang, D., & Leung, H. F. (2016). Improved automatic keyword extraction given more semantic knowledge. In International conference on database systems for advanced applications (pp. 112–125). Cham: Springer.

  • Yang, L., Li, K., & Huang, H. (2018). A new network model for extracting text keywords. Scientometrics,116(1), 339–361.

    Google Scholar 

  • Ying, Y., Qingping, T., Qinzheng, X., Ping, Z., & Panpan, L. (2017). A graph-based approach of automatic keyphrase extraction. Procedia Computer Science,107, 248–255.

    Google Scholar 

  • Zhang, Y. C., Medo, M., Ren, J., Zhou, T., Li, T., & Yang, F. (2007). Recommendation model based on opinion diffusion. EPL (Europhysics Letters),80(6), 68003.

    MathSciNet  Google Scholar 

  • Zhang, Y., Tuo, M., Yin, Q., Qi, L., Wang, X., & Liu, T. (2020). Keywords extraction with deep neural network model. Neurocomputing,383, 113–121.

    Google Scholar 

  • Zhang, Q., Wang, Y., Gong, Y., & Huang, X. J. (2016). Keyphrase extraction using deep recurrent neural networks on twitter. In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 836–845).

  • Zhang, N., Wang, J., Ma, Y., He, K., Li, Z., & Liu, X. F. (2018). Web service discovery based on goal-oriented query expansion. Journal of Systems and Software,142, 73–91.

    Google Scholar 

  • Zhou, T., Kuscsik, Z., Liu, J. G., Medo, M., Wakeling, J. R., & Zhang, Y. C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences,107(10), 4511–4515.

    Google Scholar 

  • Zhou, T., Ren, J., Medo, M., & Zhang, Y. C. (2007). Bipartite network projection and personal recommendation. Physical Review E,76(4), 046115.

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (71771177, 71601119, 71874088), Innovation Fund for University Production, Education and Research from China’s Ministry of Education (2019J01012), and International Exchange Program for Graduate Students, Tongji University (201902027). The authors thank the editor and the anonymous reviewers for their helpful comments and suggestions in improving this manuscript.

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Correspondence to Hongwei Wang.

Appendices

Appendix 1

See Table 6.

Table 6 Initial keyword list of four subjects (Dataset: Semeval)

Appendix 2

See Table 7.

Table 7 The keywords retrieved by four methods (Dataset: Semeval)

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Yin, X., Wang, H., Yin, P. et al. A co-occurrence based approach of automatic keyword expansion using mass diffusion. Scientometrics 124, 1885–1905 (2020). https://doi.org/10.1007/s11192-020-03601-7

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