# Weakly Supervised Domain Detection

arXiv: https://arxiv.org/abs/1907.11499 · HTML: https://mlsanity.com/paper/1907.11499/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Aspect-Controllable Opinion Summarization](https://mlsanity.com/paper/2109.03171/) (2021)
- [SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling](https://mlsanity.com/paper/2311.15134/) (2023)
- [InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs](https://mlsanity.com/paper/2601.06666/) (2026)
- [Unsupervised Morphological Tree Tokenizer](https://mlsanity.com/paper/2406.15245/) (2024)

## Abstract (arXiv)

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.