Search Results for author: Laiba Mehnaz

Found 7 papers, 3 papers with code

Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders

no code implementations SMM4H (COLING) 2020 Laiba Mehnaz

This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1.

Classification Text Classification

GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations

no code implementations17 Apr 2021 Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle Lee, Anish Acharya, Rajiv Ratn Shah

Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6, 831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English.

Abstractive Text Summarization

Identifying Offensive Posts and Targeted Offense from Twitter

no code implementations19 Apr 2019 Haimin Zhang, Debanjan Mahata, Simra Shahid, Laiba Mehnaz, Sarthak Anand, Yaman Singla, Rajiv Ratn Shah, Karan Uppal

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.

Suggestion Mining from Online Reviews using ULMFiT

1 code implementation19 Apr 2019 Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Ratn Shah, Karan Uppal

In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.

Classification General Classification +3

Identification of Emergency Blood Donation Request on Twitter

1 code implementation WS 2018 Puneet Mathur, Meghna Ayyar, Sahil Chopra, Simra Shahid, Laiba Mehnaz, Rajiv Shah

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.

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