TopicAdapt- An Inter-Corpora Topics Adaptation Approach
Pritom Saha Akash Trisha Das Kevin Chen-Chuan Chang
Topic models are popular statistical tools for detecting latent semantic
topics in a text corpus. They have been utilized in various applications across
different fields. However, traditional topic models have some limitations,
including insensitivity to user guidance, sensitivity to the amount and quality
of data, and the inability to adapt learned topics from one corpus to another.
To address these challenges, this paper proposes a neural topic model,
TopicAdapt, that can adapt relevant topics from a related source corpus and
also discover new topics in a target corpus that are absent in the source
corpus. The proposed model offers a promising approach to improve topic
modeling performance in practical scenarios. Experiments over multiple datasets
from diverse domains show the superiority of the proposed model against the
state-of-the-art topic models.