Search Results for author: Puneet Mathur

Found 15 papers, 5 papers with code

3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos

no code implementations CVPR 2022 Vikram Gupta, Trisha Mittal, Puneet Mathur, Vaibhav Mishra, Mayank Maheshwari, Aniket Bera, Debdoot Mukherjee, Dinesh Manocha

We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj.

Multimodal Multi-Speaker Merger \& Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines

no code implementations ACL 2021 Ramit Sawhney, Mihir Goyal, Prakhar Goel, Puneet Mathur, Rajiv Ratn Shah

We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M{\&}A calls.


TIMERS: Document-level Temporal Relation Extraction

no code implementations ACL 2021 Puneet Mathur, Rajiv Jain, Franck Dernoncourt, Vlad Morariu, Quan Hung Tran, Dinesh Manocha

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.

Relation Classification

Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures

1 code implementation NAACL 2021 Ramit Sawhney, Puneet Mathur, Taru Jain, Akash Kumar Gautam, Rajiv Ratn Shah

We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.

Classification Emotion Classification +2

Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification

no code implementations21 Feb 2021 Puneet Mathur, Trisha Mittal, Dinesh Manocha

We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram~(EEG) and Eye movement~(EM) data in order to help differentiate between normal reading and task-oriented reading.

EEG Graph Learning

#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement

no code implementations14 Dec 2019 Akash Gautam, Puneet Mathur, Rakesh Gosangi, Debanjan Mahata, Ramit Sawhney, Rajiv Ratn Shah

In this paper, we present a dataset containing 9, 973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts.

Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

no code implementations NAACL 2019 Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah

The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment.

Classification General Classification +2

Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning

no code implementations WS 2018 Ramit Sawhney, Manch, Prachi a, Puneet Mathur, Rajiv Shah, Raj Singh

The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention.

Classification General Classification +1

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.

Did you offend me? Classification of Offensive Tweets in Hinglish Language

1 code implementation WS 2018 Puneet Mathur, Ramit Sawhney, Meghna Ayyar, Rajiv Shah

The use of code-switched languages (\textit{e. g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages.

Abuse Detection General Classification +2

Detecting Offensive Tweets in Hindi-English Code-Switched Language

no code implementations WS 2018 Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata

The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script.

General Classification Hate Speech Detection +1

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