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A Complex Network Approach to Distributional Semantic Models

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  • Akira Utsumi

Abstract

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

Suggested Citation

  • Akira Utsumi, 2015. "A Complex Network Approach to Distributional Semantic Models," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-34, August.
  • Handle: RePEc:plo:pone00:0136277
    DOI: 10.1371/journal.pone.0136277
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    Cited by:

    1. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
    2. László Kovács, 2019. "Insights from Brand Associations: Alcohol Brands and Automotive Brands in the Mind of the Consumer," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 31(1), pages 97-121.
    3. Ciaglia, Floriana & Stella, Massimo & Kennington, Casey, 2023. "Investigating preferential acquisition and attachment in early word learning through cognitive, visual and latent multiplex lexical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).

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