Simra Shahid


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HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model
Simra Shahid | Tanay Anand | Nikitha Srikanth | Sumit Bhatia | Balaji Krishnamurthy | Nikaash Puri
Findings of the Association for Computational Linguistics: ACL 2023

Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry-based Hierarchical Topic Model - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialize in granularity from generic higher-level topics to specific lower-level topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline. We have made the source code for our algorithm publicly accessible.


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MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter
Debanjan Mahata | Sarthak Anand | Haimin Zhang | Simra Shahid | Laiba Mehnaz | Yaman Kumar | Rajiv Ratn Shah
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.

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MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter
Debanjan Mahata | Haimin Zhang | Karan Uppal | Yaman Kumar | Rajiv Ratn Shah | Simra Shahid | Laiba Mehnaz | Sarthak Anand
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we present our approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub Task A involves identifying if a given tweet is offensive and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub Task A is based on an ensemble of Convolutional Neural Network and Bidirectional LSTM, whereas for Sub Task B, we rely on a set of heuristics derived from the training data. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub Task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants achieving a macro F1 of 0.695.

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MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit
Sarthak Anand | Debanjan Mahata | Kartik Aggarwal | Laiba Mehnaz | Simra Shahid | Haimin Zhang | Yaman Kumar | Rajiv Shah | Karan Uppal
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we present our approach to tackle the Suggestion Mining from Online Reviews and Forums Sub-Task A. Given a review, we are asked to predict whether the review consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide analysis of the model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.


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Identification of Emergency Blood Donation Request on Twitter
Puneet Mathur | Meghna Ayyar | Sahil Chopra | Simra Shahid | Laiba Mehnaz | Rajiv Shah
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89%.