mlsanityData to 7 Oct 2026

TeCoMiner: Topic Discovery Through Term Community Detection

arXiv 2103.12882 ↗First posted 23 Mar 2021cs.CLcited by 1

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Abstract (arXiv)

This note is a short description of TeCoMiner, an interactive tool for exploring the topic content of text collections. Unlike other topic modeling tools, TeCoMiner is not based on some generative probabilistic model but on topological considerations about co-occurrence networks of terms. We outline the methods used for identifying topics, describe the features of the tool, and sketch an application, using a corpus of policy related scientific news on environmental issues published by the European Commission over the last decade.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

5 of 22 references.

References · 5

  1. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  2. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  3. 2016Community detection in networks: A user guidecited by 105
  4. 2017A network approach to topic modelscited by 25
  5. 2014A high-reproducibility and high-accuracy method for automated topic classificationcited by 6

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Citing papers per year: 2021: 1.

1 paper.