Document (#42104)

Author
Hu, K.
Luo, Q.
Qi, K.
Yang, S.
Mao, J.
Fu, X.
Zheng, J.
Wu, H.
Guo, Y.
Zhu, Q.
Title
Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis
Source
Information processing and management. 56(2019) no.4, S.1185-1203
Year
2019
Abstract
Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.
Content
Vgl.: https://doi.org/10.1016/j.ipm.2019.02.014.
Theme
Semantisches Umfeld in Indexierung u. Retrieval

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