In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: The utility of Twitter data as a medium to support populationlevel mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.

A: Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
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Q: Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.

A: Explanation Trees for Causal Bayesian Networks
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Q: We develop a new model for Interactive Question Answering (IQA), using GatedRecurrent-Unit recurrent networks (GRUs) as encoders for statements and questions, and another GRU as a decoder for outputs. Distinct from previous work, our approach employs context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. Employing these mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input. When available, user’s feedback is encoded and directly applied to update sentence-level attention to infer the answer. Extensive experiments on QA and IQA datasets demonstrate quantitatively the effectiveness of our model with significant improvement over conventional QA models.

A:
A CONTEXT-AWARE ATTENTION NETWORK FOR INTERACTIVE QUESTION ANSWERING
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