Search Results for author:
Found 17 papers, 5 papers with code
DocTime: A Document-level Temporal Dependency Graph Parser
We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph.
VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls
Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting.
LayerDoc: Layer-wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents
Experiments show that our approach outperforms competitive baselines by 10-15% on three diverse datasets of forms and mobile app screen layouts for the tasks of spatial region classification, higher-order group identification, layout hierarchy extraction, reading order detection, and word grouping.
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos
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
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
We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.
Ranked #3 on
Temporal Relation Classification
on TB-Dense
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures
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.
Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality
We use an LSTM-based learning model for emotion perception.
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification
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.
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms
Twitter is a social media platform where users express opinions over a variety of issues.
#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement
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
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.
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media
Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research.
Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning
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.
Identification of Emergency Blood Donation Request on Twitter
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
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.
Detecting Offensive Tweets in Hindi-English Code-Switched Language
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.
