Ashequl Qadir

Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases

We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFECTION, ANGER/RAGE, FEAR/ANXIETY, JOY, and SADNESS/DISAPPOINTMENT. Starting with seed hashtags to label an initial set of tweets, we train emotion classifiers and use them to learn new emotion hashtags and hashtag patterns. This process then repeats in a bootstrapping framework. Emotion phrases are also extracted from the learned hashtags and used to create phrase-based emotion classifiers. We show that the learned set of emotion indicators yields a substantial improvement in F-scores, ranging from +%5 to +%18 over baseline classifiers.

Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks

Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published. Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting. Deep learning has significantly improved text classification performance in recent years and can potentially enhance ADE classification in tweets. However, these models typically require large corpora with human expert-derived labels, and such resources are very expensive to generate and are hardly available. Semi-supervised deep learning models, which offer a plausible alternative to fully supervised models, involve the use of a small set of labeled data and a relatively larger collection of unlabeled data for training. Traditionally, these models are trained on labeled and unlabeled data from similar topics or domains. In reality, millions of tweets generated daily often focus on disparate topics, and this could present a challenge for building deep learning models for ADE classification with random Twitter stream as unlabeled training data. In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem. We demonstrate that, with the selective use of a variety of unlabeled data, our semi-supervised CNN models outperform a strong state-of-the-art supervised classification model by +9.9% F1-score. We evaluated our models on the Twitter data set used in the PSB 2016 Social Media Shared Task. Our results present the new state-of-the-art for this data set.

Activity Modeling in Email.

We introduce a latent activity model for workplace emails, positing that communication at work is purposeful and organized by activities. We pose the problem as probabilistic inference in graphical models that jointly capture the interplay between latent activities and the email contexts they govern, such as the recipients, subject and body. The model parameters are learned using maximum likelihood estimation with an expectation maximization algorithm. We present three variants of the model that incorporate the recipients, co-occurrence of the recipients, and email body and subject. We demonstrate the model’s effectiveness in an email recipient recommendation task and show that it outperforms a state-of-the-art generative model. Additionally, we show that the activity model can be used to identify email senders who engage in similar activities, resulting in further improvements in recipient recommendation.

Learning to Recognize Affective Polarity in Similes

A simile is a comparison between two essentially unlike things, such as “Jane swims like a dolphin”. Similes often express a positive or negative sentiment toward something, but recognizing the polarity of a simile can depend heavily on world knowledge. For example, “memory like an elephant” is positive, but “memory like a sieve” is negative. Our research explores methods to recognize the polarity of similes on Twitter. We train classifiers using lexical, semantic, and sentiment features, and experiment with both manually and automatically generated training data. Our approach yields good performance at identifying positive and negative similes, and substantially outperforms existing sentiment resources.

Attention-Based Medical Caption Generation with Image Modality Classification and Clinical Concept Mapping

This paper proposes an attention-based deep learning framework for caption generation from medical images. We also propose to utilize the same framework for clinical concept prediction to improve caption generation by formulating the task as a case of sequence-to-sequence learning. The predicted concept IDs are then mapped to corresponding terms in a clinical ontology to generate an image caption. We also investigate if learning to classify images based on the modality e.g. CT scan, MRI etc. can aid in generating precise captions.