Abstract
Aiming at problems of the “same amount with different qualities’’ phenomenon and the lack of semantics in co-occurring terms, this paper proposed a new semantic measurement method in co-word analysis. The method firstly gave different weights to document units based on the Pointwise Mutual Information (PMI) method, and then extended them to the generation process of the Latent Dirichlet Allocation (LDA) model to extract core keywords. Then the word-2vec model was used to transform the Top-N keywords into low-dimensional value distributions, and the sematic correlation among keywords were calculated based on the length of windows. Finally, data from the domain of “deep learning” was used to verify the scientificity and effectiveness of the method. Comparing the results of general co-word analysis with our proposed method in terms of clustering analysis, network parameters, distribution structures and other aspects, we can find that our method is scientific and effective in considering different feasibilities of terms and their semantic correlations.
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Callon, M., Courtial, J.P., Turner, W.A., et al.: Form translations to problematic networks: an introduction to co-word analysis. Soc. Sci. Inf. 22(2), 191–235 (1983)
Ding, Y., Chowdhury, G.G., Foo, S.: Bibliometric cartography of information retrieval research by using co-word analysis. Inf. Process. Manag. 37(6), 817–842 (2001)
Guo, S., Zhang, G.Z., Ju, Q.H., et al.: The evolution of conceptual diversity in economics titles from 1890 to 2012. Scientometrics 102(3), 2073–2088 (2015)
Guardiola, R., Sanz, J., Wanden, C.: Medical subject headings versus psychological association index terms: indexing eating disorders. Scientometrics 94(1), 305–311 (2013)
Li, S., Sun, Y., Soergel, D.: Erratum to: a new method for automatically constructing domain-oriented term taxonomy based on weighted word co-occurrence analysis. Scientometrics 103(2), 1023–1042 (2016)
Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Informational Processing Systems, pp. 3111–3119. Neural Information Processing Systems Foundation, US (2013)
Tsatsaronis, G., Varlamis, I., Vazirgiannis, M.: Text relatedness based on a word thesaurus. J. Artif. Intell. Res. 37(1), 1–40 (2010)
Wang, Z.Y., Li, G., Li, C.Y., Li, A.: Research on the semantic-based co-word analysis. Scientometrics 90(3), 855–875 (2012)
Wilson, A.T., Chew, P.A.: Term weighting schemes for latent dirichlet allocation. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computation Linguistics, pp. 465–473. Association for Computational Linguistics, Stroudsburg (2010)
Zhang, H.P., Yu, H.K., Xiong, D.Y., et al.: Chinese lexical analyzer using hierarchical hidden Markov model. In: Sighan Workshop on Chinese Language Processing, vol. 17, no. 8, pp. 63–70 (2003)
Zhang, K.Y., Zhu, K.Q.: An association network for computing semantic relatedness. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 593–600 (2015)
Acknowledgment
This paper is supported by the Chinese NSFC International Cooperation Program. Research on Intelligent Home Care Platform based on Chronic Disease Knowledge Management (71661167007). It is also partially sponsored by the project by the National Natural Science Foundation of China (71420107026) and National Nature Science Foundation of China (Grant No. 71704138).
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Zhou, L., Ba, Z., Fan, H., Zhang, B. (2018). Research on the Semantic Measurement in Co-word Analysis. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds) Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science(), vol 10766. Springer, Cham. https://doi.org/10.1007/978-3-319-78105-1_45
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DOI: https://doi.org/10.1007/978-3-319-78105-1_45
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