Abstract: 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.

Introduction: Text classification is a fundamental task in Natural Language processing which has been found useful in a wide spectrum of applications ranging from search engines enabling users to identify content on websites, sentiment and social media analysis, customer relationship management systems, and spam detection. Over the past several years, text classification has been predominantly modeled as a supervised learning problem (e.g., BIBREF0 , BIBREF1 , BIBREF2 ) for which appropriately labeled data must be collected. Such data is often domain-dependent (i.e., covering specific topics such as those relating to “Business” or “Medicine”) and a classifier trained using data from one domain is likely to perform poorly on another. For example, the phrase “the mouse died quickly” may indicate negative sentiment in a customer review describing the hand-held pointing device or positive sentiment when describing a laboratory experiment performed on a rodent. The ability to handle a wide variety of domains has become more pertinent with the rise of data-hungry machine learning techniques like neural networks and their application to a plethora of textual media ranging from news articles to twitter, blog posts, medical journals, Reddit comments, and parliamentary debates BIBREF0 , BIBREF3 , BIBREF4 , BIBREF5 . The question of how to best deal with multiple domains when training data is available for one or few of them has met with much interest in the literature. The field of domain adaptation BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 aims at improving the learning of a predictive function in a target domain where there is little or no labeled data, using knowledge transferred from a source domain where sufficient labeled data is available. Another line of work BIBREF11 , BIBREF12 , BIBREF13 assumes that labeled data may exist for multiple domains, but in insufficient amounts to train classifiers for one or more of them. The aim of multi-domain text classification is to leverage all the available resources in order to improve system performance across domains simultaneously. In this paper we investigate the question of how domain-specific data might be obtained in order to enable the development of text classification tools as well as more domain aware applications such as summarization, question answering, and information extraction. We refer to this task as domain detection and assume a fairly common setting where the domains of a corpus collection are known and the aim is to identify textual segments which are domain-heavy, i.e., documents, sentences, or phrases providing evidence for a given domain. Domain detection can be formulated as a multilabel classification problem, where a model is trained to recognize domain evidence at the sentence-, phrase-, or word-level. By definition then, domain detection would require training data with fine-grained domain labels, thereby increasing the annotation burden; we must provide labels for training domain detectors and for modeling the task we care about in the first place. In this paper we consider the problem of fine-grained domain detection from the perspective of Multiple Instance Learning (MIL; BIBREF14 ) and develop domain models with very little human involvement. Instead of learning from individually labeled segments, our model only requires document-level supervision and optionally prior domain knowledge and learns to introspectively judge the domain of constituent segments. Importantly, we do not require document-level domain annotations either since we obtain these via distant supervision by leveraging information drawn from Wikipedia. Our domain detection framework comprises two neural network modules; an encoder learns representations for words and sentences together with prior domain information if the latter is available (e.g., domain definitions), while a detector generates domain-specific scores for words, sentences, and documents. We obtain a segment-level domain predictor which is trained end-to-end on document-level labels using a hierarchical, attention-based neural architecture BIBREF15 . We conduct domain detection experiments on English and Chinese and measure system performance using both automatic and human-based evaluation. Experimental results show that our model outperforms several strong baselines and is robust across languages and text genres, despite learning from weak supervision. We also showcase our model's application potential for text summarization. Our contributions in this work are threefold; we propose domain detection, as a new fine-grained multilabel learning problem which we argue would benefit the development of domain aware NLP tools; we introduce a weakly supervised encoder-detector model within the context of multiple instance learning; and demonstrate that it can be applied across languages and text genres without modification.

Question: What domains are detected in this paper?
Answer: Answer with content missing: (Experimental setup not properly rendered) In our experiments we used seven target domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW),
“Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). Exceptionally, GEN does
not have a natural root category.