Browsing by Subject "topic models"

Sort by: Order: Results:

Now showing items 1-3 of 3
  • An, Yu (Helsingin yliopisto, 2020)
    Maps of science, or cartography of scientific fields, provide insights into the state of scientific knowledge. Analogous to geographical maps, maps of science present the fields as positions and show the paths connecting each other, which can serve as an intuitive illustration for the history of science or a hint to spot potential opportunities for collaboration. In this work, I investigate the reproducibility of a method to generate such maps. The idea of the method is to derive representations representations for the given scientific fields with topic models and then perform hierarchical clustering on these, which in the end yields a tree of scientific fields as the map. The result is found unreproducible, as my result obtained on the arXiv data set (~130k articles from arXiv Computer Science) shows an inconsistent structure from the one in the reference study. To investigate the cause of the inconsistency, I derive a second set of maps using the same method and an adjusted data set, which is constructed by re-sampling the arXiv data set to a more balanced distribution. The findings show the confounding factors in the data cannot account for the inconsistency; instead, it should be due to the stochastic nature of the unsupervised algorithm. I also improve the approach by using ensemble topic models to derive representations. It is found the method to derive maps of science can be reproducible when it uses an ensemble topic model fused from a sufficient number of base models.
  • Huang, Chien-yu; Casey, Arlene; Glowacka, Dorota; Medlar, Alan (ACM, 2019)
    Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers. In this article, we use topic modelling to represent content in abstracts and full-text articles. Using Computer Science as a case study, we demonstrate that how well the abstract summarises the full-text is subfield-dependent. Indeed, we show that abstract representativeness has a direct impact on retrieval performance, with poorer abstracts leading to degraded performance. Finally, we present evidence that how well an abstract represents the full-text of an article is not random, but is a consequence of style and writing conventions in different subdisciplines and can be used to infer an "evolutionary" tree of subfields within Computer Science.
  • Ylä-Anttila, Tuukka; Eranti, Veikko; Kukkonen, Anna (2022)
    We argue that 'topics' of topic models can be used as a useful proxy for frames if (1) frames are operationalized as connections between concepts; (2) theme-specific data are used; and (3) topics are validated in terms of frame analysis. Demonstrating this, we analyse 12 climate change frames used by NGOs, governments and experts in Indian and US media, gathered by topic modeling. We contribute methodologically to topic modeling in the social sciences and frame analysis of public debates, and empirically to research on climate change media debates.