Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2022

Abstract

Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to each neuron. In this paper, we present a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. Firstly, building on the original information from a trained NTM, we generate a pool of potential candidate “composite topics” by exploiting possible co-occurrences within the original set of topics, which decouples the strict interpretation of topics from the original NTM. This is followed by a combinatorial formulation to select a final set of composite topics, which we evaluate for coherence and diversity on a large external corpus. Lastly, we employ a user study to derive further insights on the reinterpretation process.

Keywords

topic modeling, composite topics, empirical study, machine learning, neural networks

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

City or Country

USA

Additional URL

https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.242/

Download

Share

COinS