A5085370631- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Biomedical Text Mining and Ontologies
- Speech and dialogue systems
- Multimodal Machine Learning Applications
- Misinformation and Its Impacts
- Hate Speech and Cyberbullying Detection
- Spam and Phishing Detection
- Mental Health via Writing
- Text Readability and Simplification
- Software Engineering Research
- Emotion and Mood Recognition
- Humor Studies and Applications
- Expert finding and Q&A systems
- Web Data Mining and Analysis
- Advanced Clustering Algorithms Research
- Semantic Web and Ontologies
- Advanced Image and Video Retrieval Techniques
- Metaheuristic Optimization Algorithms Research
- Gene expression and cancer classification
- AI in Service Interactions
- Domain Adaptation and Few-Shot Learning
Indian Institute of Technology Jodhpur
2024-2025
Indian Institute of Technology Patna
2016-2025
Oak Ridge National Laboratory
2023
RIKEN Center for Advanced Intelligence Project
2023
Mongolia International University
2023
Bar-Ilan University
2021
University of Helsinki
2021
Tel Aviv University
2021
Technical University of Darmstadt
2021
University of Copenhagen
2021
The automatic extraction of chemical information from text requires the recognition entity mentions as one its key steps. When developing supervised named (NER) systems, availability a large, manually annotated corpus is desirable. Furthermore, large corpora permit robust evaluation and comparison different approaches that detect chemicals in documents. We present CHEMDNER corpus, collection 10,000 PubMed abstracts contain total 84,355 labeled by expert chemistry literature curators,...
Emotions and sentiments are subjective in nature. They differ on a case-to-case basis. However, predicting only the emotion sentiment does not always convey complete information. The degree or level of emotions often plays crucial role understanding exact feeling within single class (e.g., `good' versus `awesome'). In this paper, we propose stacked ensemble method for intensity by combining outputs obtained from several deep learning classical feature-based models using multi-layer...
Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya. Proceedings of the 2019 Conference North American Chapter Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Multi-modal sentiment analysis offers various challenges, one being the effective combination of different input modalities, namely text, visual and acoustic. In this paper, we propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level prediction. The proposed approach applies on multi-utterance representations tries to learn contributing features amongst them. We evaluate our two benchmark datasets, viz. CMU...
Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment takes this one step further by enabling businesses to extract aspect specific sentimental information. In paper, we present a novel approach of incorporating the neighboring aspects related information into classification target using memory networks. We show that our method outperforms state art 1.6% on average two distinct domains: restaurant and laptop.
In this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, thereby propose a multi-task deep learning framework solve all these three problems simultaneously in multi-modal conversational scenario. We, at first, manually annotate the recently released MUStARD dataset with emotion classes, both implicit explicit. For multi-tasking, two attention mechanisms, viz. Inter-segment Inter-modal Attention (Ie-Attention) Intra-segment (Ia-Attention). The main motivation of...
In this paper, we propose a novel method for combining deep learning and classical feature based models using Multi-Layer Perceptron (MLP) network financial sentiment analysis. We develop various on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Gated Recurrent Unit (GRU). These are trained top of pre-trained, autoencoder-based, word embeddings lexicon features. An ensemble is constructed by these supervised model Support Vector Regression (SVR). evaluate our proposed...
We propose a multi-task ensemble framework that jointly learns multiple related problems. The model aims to leverage the learned representations of three deep learning models (i.e., CNN, LSTM and GRU) hand-crafted feature representation for predictions. Through framework, we address four problems emotion sentiment analysis, i.e., "emotion <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classification</i> &...
Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, extraction, question answering systems others. This paper reports about the development NER system Bengali Hindi using Support Vector Machine (SVM). Though this state art learning technique has been widely applied in several...
Dushyant Singh Chauhan, Md Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint (EMNLP-IJCNLP). 2019.