  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.
