Search Results for author:
Found 61 papers, 14 papers with code
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
We use this network to regress the depression level.
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits.
Ranked #4 on
Automated Essay Scoring
on ASAP
Self-Supervised Image-to-Text and Text-to-Image Synthesis
A comprehensive understanding of vision and language and their interrelation are crucial to realize the underlying similarities and differences between these modalities and to learn more generalized, meaningful representations.
Global Object Proposals for Improving Multi-Sentence Video Descriptions
Recently, many works are proposed on the generation of multi-sentence video descriptions.
Ranked #2 on
Dense Video Captioning
on ActivityNet Captions
MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries
This work introduces the task of multimodal medical question summarization for codemixed input in a low-resource setting.
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS).
Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation
Over the past few years, the use of the Internet for healthcare-related tasks has grown by leaps and bounds, posing a challenge in effectively managing and processing information to ensure its efficient utilization.
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
We leveraged the functional property of Mean Square Error, which is Lipschitz continuous to compute learning rate in shallow neural networks.
Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction
The recent advancement of pre-trained Transformer models has propelled the development of effective text mining models across various biomedical tasks.
Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization
We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework.
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation
While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying.
Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text.
Leveraging Medical Sentiment to Understand Patients Health on Social Media
A large percentage of this population is actively engaged in health social networks to share health-related information.
Temporal Orientation of Tweets for Predicting Income of Users
Automatically estimating a user{'}s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics.
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection
The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed.
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment
In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog.
Semi-supervised Clustering of Medical Text
Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications.
Deep Learning Architecture for Patient Data De-identification in Clinical Records
However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients.
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering
Current paper explores the use of multi-view learning for search result clustering.
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework
We achieved an accuracy of 99. 83% for 20% testing data of NSL-KDD dataset and 99. 65% accuracy for 10-fold cross-validation on Kyoto dataset.
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.
Multi-Modal Summary Generation using Multi-Objective Optimization
Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques.
Towards Emotion-aided Multi-modal Dialogue Act Classification
In this work, we address the role of \textit{both} multi-modality and emotion recognition (ER) in DAC.
Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification
As a first step towards enabling the development of multimodal approaches for PPI identification, we have developed two multi-modal datasets which are extensions and multi-modal versions of two popular benchmark PPI corpora (BioInfer and HRPD50).
Assessing the Severity of Health States based on Social Media Posts
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework
Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text.
Semantic Extractor-Paraphraser based Abstractive Summarization
The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models.
Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter
Experimental results indicate that the proposed framework boosts the performance of the primary task, i. e., TA classification (TAC) by benefitting from the two secondary tasks, i. e., Sentiment and Emotion Analysis compared to its uni-modal and single task TAC (tweet act classification) variants.
EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media
Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text.
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media
This task has been organized for several languages, e. g., Arabic, Danish, English, Greek and Turkish.
A Survey on Multi-modal Summarization
The new era of technology has brought us to the point where it is convenient for people to share their opinions over an abundance of platforms.
A Multi-modal Personality Prediction System
Automatic prediction of personality traits has many real-life applications, e. g., in forensics, recommender systems, personalized services etc..
Personality Trait Recognition by Face
Recommendation Systems
D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT
Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage.
Annotated Corpus of Tweets in English from Various Domains for Emotion Detection
We propose a Hierarchical Attention-based deep neural network for Emotion Detection (HAtED).
IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20
In this paper, we present the IIIT Bhagalpur and IIT Patna team’s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020.
IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020
The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art.
Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction
A transformer-based Grammar Error Correction model GECToR (2020), is used as a post-processing step for better fluency.
Improving Diversity and Reducing Redundancy in Paragraph Captions
The paragraphs generated from standard image captioning models lack in language diversity and contain redundant information.
WIDAR -- Weighted Input Document Augmented ROUGE
The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques.
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony
Mental Health Disorders continue plaguing humans worldwide.
MAKED: Multi-lingual Automatic Keyword Extraction Dataset
Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification.
A Scaled Encoder Decoder Network for Image Captioning in Hindi
The proposed method’s performance is compared with state-of-the-art methods in terms of BLEU scores and manual evaluation (in terms of adequacy and fluency).
Wikipedia Current Events Summarization using Particle Swarm Optimization
The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods.
A Survey on Medical Document Summarization
The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally.
Large Scale Multi-Lingual Multi-Modal Summarization Dataset
Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities.
Few-shot Anomaly Detection in Text with Deviation Learning
In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution.
Hi Model, generating 'nice' instead of 'good' is not as bad as generating 'rice'! Towards Context and Semantic Infused Dialogue Generation Loss Function and Evaluation Metric
These lexical-based metrics have the following key limitations: (a) word-to-word matching without semantic consideration: It assigns the same credit for failure to generate 'nice' and 'rice' for 'good'.
CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare
This multimodal approach not only enhances the decision-making process in healthcare but also fosters a more nuanced understanding of patient queries, laying the groundwork for future research in personalized and responsive medical care
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant
In recent times, there has been an increasing awareness about imminent environmental challenges, resulting in people showing a stronger dedication to taking care of the environment and nurturing green life.
Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations
A Contrastive Language-Image Pretraining (CLIP) projection-based multimodal shared-private multitask approach has been proposed for visual and textual explanation of a meme.
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters.
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution.
