Search Results for author: Sriparna Saha

Found 61 papers, 14 papers with code

IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20

no code implementations EMNLP (sdp) 2020 Saichethan Reddy, Naveen Saini, Sriparna Saha, Pushpak Bhattacharyya

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.

MAKED: Multi-lingual Automatic Keyword Extraction Dataset

no code implementations LREC 2022 Yash Verma, Anubhav Jangra, Sriparna Saha, Adam Jatowt, Dwaipayan Roy

Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification.

Clustering Keyword Extraction

A Scaled Encoder Decoder Network for Image Captioning in Hindi

no code implementations ICON 2021 Santosh Kumar Mishra, Sriparna Saha, Pushpak Bhattacharyya

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).

Image Captioning

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

no code implementations5 Feb 2024 Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha

This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters.

Prompt Engineering Question Answering

Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations

1 code implementation18 Jan 2024 Prince Jha, Krishanu Maity, Raghav Jain, Apoorv Verma, Sriparna Saha, Pushpak Bhattacharyya

A Contrastive Language-Image Pretraining (CLIP) projection-based multimodal shared-private multitask approach has been proposed for visual and textual explanation of a meme.

Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation

1 code implementation17 Jan 2024 Krishanu Maity, Prince Jha, Raghav Jain, Sriparna Saha, Pushpak Bhattacharyya

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.

Sentence Sentiment Analysis

An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant

1 code implementation10 Jan 2024 Mohit Tomar, Abhisek Tiwari, Tulika Saha, Prince Jha, Sriparna Saha

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.

Dialogue Generation Language Modelling

Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation

1 code implementation10 Jan 2024 Abhisek Tiwari, Shreyangshu Bera, Sriparna Saha, Pushpak Bhattacharyya, Samrat Ghosh

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.

Intent Recognition

CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare

no code implementations16 Dec 2023 Akash Ghosh, Arkadeep Acharya, Raghav Jain, Sriparna Saha, Aman Chadha, Setu Sinha

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

Decision Making

Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization

1 code implementation27 Sep 2023 Abhisek Tiwari, Anisha Saha, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar

We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework.

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

no code implementations11 Sep 2023 Abhisek Tiwari, Muhammed Sinan, Kaushik Roy, Amit Sheth, Sriparna Saha, Pushpak Bhattacharyya

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'.

Attribute Dialogue Generation +1

Few-shot Anomaly Detection in Text with Deviation Learning

no code implementations22 Aug 2023 Anindya Sundar Das, Aravind Ajay, Sriparna Saha, Monowar Bhuyan

In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution.

Anomaly Detection Few-Shot Learning +1

Large Scale Multi-Lingual Multi-Modal Summarization Dataset

no code implementations13 Feb 2023 Yash Verma, Anubhav Jangra, Raghvendra Kumar, Sriparna Saha

Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities.

Information Retrieval Retrieval +1

A Survey on Medical Document Summarization

no code implementations3 Dec 2022 Raghav Jain, Anubhav Jangra, Sriparna Saha, Adam Jatowt

The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally.

Document Summarization

WIDAR -- Weighted Input Document Augmented ROUGE

no code implementations23 Jan 2022 Raghav Jain, Vaibhav Mavi, Anubhav Jangra, Sriparna Saha

The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques.

Text Summarization

Self-Supervised Image-to-Text and Text-to-Image Synthesis

1 code implementation9 Dec 2021 Anindya Sundar Das, Sriparna Saha

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.

Image Generation Sentence +1

A Survey on Multi-modal Summarization

no code implementations11 Sep 2021 Anubhav Jangra, Sourajit Mukherjee, Adam Jatowt, Sriparna Saha, Mohammad Hasanuzzaman

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.

Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

1 code implementation Findings (ACL) 2021 Sriram Pingali, Shweta Yadav, Pratik Dutta, Sriparna Saha

The recent advancement of pre-trained Transformer models has propelled the development of effective text mining models across various biomedical tasks.

Relation Relation Extraction +1

Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter

no code implementations NAACL 2021 Tulika Saha, Apoorva Upadhyaya, Sriparna Saha, Pushpak Bhattacharyya

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.

Classification Emotion Recognition

Semantic Extractor-Paraphraser based Abstractive Summarization

no code implementations ICON 2020 Anubhav Jangra, Raghav Jain, Vaibhav Mavi, Sriparna Saha, Pushpak Bhattacharyya

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models.

Abstractive Text Summarization

EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media

no code implementations SEMEVAL 2020 Chandresh Kanani, Sriparna Saha, Pushpak Bhattacharyya

Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text.


Assessing the Severity of Health States based on Social Media Posts

no code implementations21 Sep 2020 Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

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.

Multiview Learning Natural Language Understanding

Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework

no code implementations20 Sep 2020 Shweta Yadav, Srivatsa Ramesh, Sriparna Saha, Asif Ekbal

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.

Multi-Task Learning Relation +1

Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification

no code implementations ACL 2020 Pratik Dutta, Sriparna Saha

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).

Multi-Modal Summary Generation using Multi-Objective Optimization

no code implementations19 May 2020 Anubhav Jangra, Sriparna Saha, Adam Jatowt, Mohammad Hasanuzzaman

Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques.

AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence

2 code implementations19 May 2020 Rohan Mohapatra, Snehanshu Saha, Carlos A. Coello Coello, Anwesh Bhattacharya, Soma S. Dhavala, Sriparna Saha

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.

Mathematical Proofs

Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets

1 code implementation18 May 2020 Shailesh Sridhar, Snehanshu Saha, Azhar Shaikh, Rahul Yedida, Sriparna Saha

We leveraged the functional property of Mean Square Error, which is Lipschitz continuous to compute learning rate in shallow neural networks.

Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework

no code implementations16 May 2019 Chanchal Suman, Somanath Tripathy, Sriparna Saha

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.

feature selection Intrusion Detection

Leveraging Medical Sentiment to Understand Patients Health on Social Media

no code implementations30 Jul 2018 Shweta Yadav, Joy Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

A large percentage of this population is actively engaged in health social networks to share health-related information.

Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts

no code implementations5 Jul 2018 Shweta Yadav, Ankit Kumar, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

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.

Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment

no code implementations NAACL 2018 Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya, Amit Sheth

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.

General Classification Multi-Task Learning +1

Temporal Orientation of Tweets for Predicting Income of Users

no code implementations ACL 2017 Mohammed Hasanuzzaman, Sabyasachi Kamila, M Kaur, eep, Sriparna Saha, Asif Ekbal

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.

regression TAG +1

Semi-supervised Clustering of Medical Text

no code implementations WS 2016 Pracheta Sahoo, Asif Ekbal, Sriparna Saha, Diego Moll{\'a}, N, Kaushik an

Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications.


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