Using virtual edges to improve the discriminability of co-occurrence text networks

Laura V.C. Quispe, Jorge A.V. Tohalino and Diego R. Amancio

Physica A: Statistical Mechanics and its Applications, 2021, vol. 562, issue C

Abstract: Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text. Here we investigate whether the use of word embeddings as a tool to create virtual links in co-occurrence networks may improve the quality of classification systems. Our results revealed that the discriminability in the stylometry task is improved when using Glove, Word2Vec and FastText. In addition, we found that optimized results are obtained when stopwords are not disregarded and a simple global thresholding strategy is used to establish virtual links. Because the proposed approach is able to improve the representation of texts as complex networks, we believe that it could be extended to study other natural language processing tasks. Likewise, theoretical languages studies could benefit from the adopted enriched representation of word co-occurrence networks.

Keywords: Network science; Language networks; Text networks; Co-occurrence networks; Semantic networks; Word embeddings (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:562:y:2021:i:c:s037843712030707x

DOI: 10.1016/j.physa.2020.125344

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