We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt ), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable. Previous work relied on syntactic features and complex neural models. We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed. The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat.
The CEGS N-GRID 2016 Shared Task (Filannino, Stubbs, Uzuner (2017)) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology. Random Forests provides a generalization of the extracted, case-specific features in our representation. The best variant presented here scored an inverse mean absolute error (MAE) of 80.64%. A concise concept-based representation, paired with identification of concept certainty and scope (family, patient), shows a robust performance on the task.
This article examines the Twitter and Facebook uptake of health messages from an infotainment TV show on food, as broadcasted on Belgium's Dutch-language public broadcaster. The interest in and amount of health-related media coverage is rising, and this media coverage is an important source of information for laypeople, and impacts their health behaviours and therapy compliance. However, the role of the audience has also changed; consumers of media content increasingly are produsers, and, in the case of health, expert consumers. To explore how current audiences react to health claims, we have conducted a quantitative and qualitative content analysis of Twitter and Facebook reactions to an infotainment show about food and nutrition. We examine (1) to which elements in the show the audience reacts, to gain insight in the traction the nutrition-related content generates and (2) whether audience members are accepting or resisting the health information in the show. Our findings show that the information on health and production elicit the most reactions, and that health information incites a lot of refutation, low acceptance and a lot of suggestions on new information or new angles to complement the show's information.
Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.
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