Summary of Cftm: Continuous Time Fractional Topic Model, by Kei Nakagawa et al.
CFTM: Continuous time fractional topic model
by Kei Nakagawa, Kohei Hayashi, Yugo Fujimoto
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Applications (stat.AP)
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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 new method for dynamic topic modeling called Continuous Time Fractional Topic Model (cFTM). The cFTM incorporates fractional Brownian motion (fBm) to capture positive or negative correlations between topic and word distributions over time, revealing long-term dependencies or roughness. Theoretical analysis shows that the cFTM can effectively identify these patterns in both topic and word distributions, mirroring fBm characteristics. The model’s parameter estimation process is comparable to traditional topic models like LDA. To demonstrate the cFTM’s properties, an empirical study using economic news articles is conducted, showing its ability to track long-term dependencies or roughness over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze how topics and words change over time. The Continuous Time Fractional Topic Model (cFTM) helps identify patterns that can last for a long time. It does this by using something called fractional Brownian motion, which is good at finding these patterns. The researchers tested the cFTM with economic news articles and found that it works well. |
