Cross-Domain Labeled LDA for Cross-Domain Text Classification

TL;DR

This paper introduces CDL-LDA, a novel cross-domain topic model that uses group-level semantic alignment and partial supervision to improve text classification across different domains.

Contribution

It proposes a new group alignment method and partial supervision mechanism integrated into a cross-domain LDA model for better domain adaptation.

Findings

Improved classification accuracy on 20Newsgroup and Reuters datasets.

Enhanced topic coherence and semantic group learning.

Effective reduction of domain discrepancy through group-level alignment.

Abstract

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.