Summary of Topic Modelling: Going Beyond Token Outputs, by Lowri Williams et al.
Topic Modelling: Going Beyond Token Outputs
by Lowri Williams, Eirini Anthi, Laura Arman, Pete Burnap
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel approach to extend the output of traditional topic modeling methods beyond a list of isolated tokens. The current state-of-the-art relies on external language sources, which can be problematic due to issues such as data unavailability, update requirements, and privacy concerns. Instead, this method utilizes the textual data itself by extracting high-scoring keywords and mapping them to the topic model’s token outputs. This approach demonstrates higher quality and usefulness in terms of interpretability compared to traditional methods, making it a valuable contribution to the field of text mining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes topic modeling better! Right now, when we get a list of topics from lots of documents, it can be hard to understand what those topics really mean. Some people have tried to fix this by using extra language sources, but that has its own problems like data going missing or needing constant updates. This new approach gets rid of those issues by using the text itself to figure out what each topic is about. It works really well and makes it easier for humans to understand what’s going on in our topics! |
Keywords
* Artificial intelligence * Token
