A Framework for Neural Topic Modeling of Text Corpora

TL;DR

FAME is an open-source framework that leverages traditional and transformer-based embeddings for improved neural topic modeling and document clustering in text corpora.

Contribution

The paper introduces FAME, a novel framework integrating diverse textual features, including BERT embeddings, for enhanced topic discovery and clustering.

Findings

Effective extraction of topics using BERT embeddings

Improved clustering of semantically similar documents

Open-source availability of the FAME framework

Abstract

Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields. In this work, inspired by the recent advancements in the Natural Language Processing domain, we introduce FAME, an open-source framework enabling an efficient mechanism of extracting and incorporating textual features and utilizing them in discovering topics and clustering text documents that are semantically similar in a corpus. These features range from traditional approaches (e.g., frequency-based) to the most recent auto-encoding embeddings from transformer-based language models such as BERT model family. To demonstrate the effectiveness of this library, we conducted experiments on the well-known News-Group dataset. The library is available online.