Search Results for author:
Found 34 papers, 14 papers with code
CIAug: Equipping Interpolative Augmentation with Curriculum Learning
CIAug achieves state-of-the-art results over existing interpolative augmentation methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks.
DMix: Distance Constrained Interpolative Mixup
Interpolation-based regularisation methods have proven to be effective for various tasks and modalities.
A Risk-Averse Mechanism for Suicidality Assessment on Social Media
Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings.
HYPHEN: Hyperbolic Hawkes Attention For Text Streams
Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities.
DMix: Adaptive Distance-aware Interpolative Mixup
Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.
A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media
Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention.
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.
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.
Ranked #1 on
Stock Market Prediction
on stocknet
(using extra training data)
Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging
There have been efforts in cross-lingual transfer learning for various tasks.
HypMix: Hyperbolic Interpolative Data Augmentation
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data.
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media.
The Impact of Differential Privacy on Group Disparity Mitigation
The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of members of such groups.
ADIMA: Abuse Detection In Multilingual Audio
Abusive content detection in spoken text can be addressed by performing Automatic Speech Recognition (ASR) and leveraging advancements in natural language processing.
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.
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.
Quantitative Day Trading from Natural Language using Reinforcement Learning
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners.
An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls
In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world{'}s biggest stock indexes, the S{\&}P 500 index.
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading
Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media
Building on clinical studies, PHASE learns phase-like progressions in users{'} historical Plutchik-wheel-based emotions to contextualize suicidal intent.
Augmenting NLP models using Latent Feature Interpolations
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.
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.
\#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media
The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being.
Investigating Political Herd Mentality: A Community Sentiment Based Approach
Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today.
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic
The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language.
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.
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.
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets
Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide.
