Graphing the Words: A Lightweight Way to Label Topics Without Heavy AI

Topic labeling and extraction have always been crucial tasks in the field of natural language processing. Traditionally, these tasks have relied heavily on complex AI models that require significant computational resources. However, a new approach has emerged that offers a lightweight alternative to deep models for labeling topics.

The approach involves augmenting topic words with semantically related terms via a knowledge graph. By using sentence embeddings, the system is able to surface relevant labels and select the most interpretable label from the graph relations. This method not only enriches topic words with related terms but also explores the relationships among them, providing a more efficient and effective way to extract topics.

With the rise of large language models (LLMs), the question of whether they can analyze graphs like professionals has also been raised. Recent research has shown promising results in label-free node classification on graphs using LLMs, indicating the potential for further advancements in this area.

In conclusion, graphing the words offers a lightweight and efficient way to label topics without heavy reliance on AI models. By leveraging knowledge graphs and exploring semantic relationships, this approach provides a valuable alternative for topic extraction in natural language processing.