Summary of Hierarchical Thematic Classification Of Major Conference Proceedings, by Arsentii Kuzmin et al.
Hierarchical thematic classification of major conference proceedings
by Arsentii Kuzmin, Alexander Aduenko, Vadim Strijov
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops a decision support system for hierarchical text classification, where experts provide a fixed hierarchical structure of topics as a tree. The system ranks topics by relevance to a given document, with experts choosing the most relevant topic to complete classification. The authors propose a weighted hierarchical similarity function to calculate topic relevance, using entropy-based word importance estimates. This approach is demonstrated through calculations and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, this paper creates a tool that helps sort topics in text collections based on how well they match a given document. Experts provide the initial hierarchy of topics, and the system suggests the most relevant ones. The authors also develop a method to determine which words are more important when comparing documents. |
Keywords
» Artificial intelligence » Classification » Text classification
