Summary of Semantic-driven Topic Modeling Using Transformer-based Embeddings and Clustering Algorithms, by Melkamu Abay Mersha et al.
Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms
by Melkamu Abay Mersha, Mesay Gemeda yigezu, Jugal Kalita
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 innovative paper proposes a novel end-to-end semantic-driven topic modeling technique that leverages advanced word and document embeddings combined with clustering algorithm, showcasing significant advancements in topic modeling methodologies. By utilizing pre-trained transformer-based language models to generate document embeddings, reducing dimensions, clustering based on semantic similarity, and generating coherent topics for each cluster, this approach effectively captures contextual semantic information, leading to more meaningful and coherent topics compared to traditional techniques like ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces a new way to find hidden patterns in documents. It’s called topic modeling, and it helps us understand what people are talking about without knowing beforehand. The current methods have limitations when trying to capture the meaning behind words. This paper presents a brand-new approach that uses special language models to create unique “word fingerprints” for each document. Then, it groups these fingerprints into categories based on their similarity, allowing us to identify important topics and patterns. |
Keywords
» Artificial intelligence » Clustering » Transformer
