Weakly Supervised Domain Detection
This addresses domain detection as a new NLP task for enhancing text classification applications, though it appears incremental in method.
The paper tackles the problem of identifying domain-heavy textual segments to improve text classification robustness, proposing an encoder-detector framework with multiple instance learning that works across granularities, languages, and genres.
In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences or phrases which are representative of and provide evidence for a given domain could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning (MIL). The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.