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The CHEMDNER corpus of chemicals and drugs and its annotation principles
The CHEMDNER corpus is presented, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task.
Fighting an Infodemic: COVID-19 Fake News Dataset
A manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19 is curate and released, and four machine learning baselines are benchmarked.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis
- Deepanway Ghosal, Md. Shad Akhtar, Dushyant Singh Chauhan, Soujanya Poria, Asif Ekbal, P. Bhattacharyya
- Computer ScienceConference on Empirical Methods in Natural…
A recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction that applies attention on multi- modal multi-utterance representations and tries to learn the contributing features amongst them.
Language Independent Named Entity Recognition in Indian Languages
- Asif Ekbal, Rejwanul Haque, Amitava Das, V. Poka, Sivaji Bandyopadhyay
- LinguisticsInternational Joint Conference on Natural…
This paper reports about the development of a Named Entity Recognition (NER) system for South and South East Asian languages, particularly for Bengali, Hindi, Telugu, Oriya and Urdu as part of the…
Named Entity Recognition using Support Vector Machine: A Language Independent Approach
The development of a NER system for Bengali and Hindi using Support Vector Machine (SVM) and an unsupervised algorithm is developed in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus.
A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi
This paper describes the development of Named Entity Recognition systems for two leading Indian languages, namely Bengali and Hindi, using the Conditional Random Field (CRF) framework and considers only the tags that denote person names, location names, organization names, number expressions, time expressions and measurement expressions.
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
- Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, E. Cambria, Asif Ekbal
- Computer ScienceEMNLP
A novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks and it is shown that this method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.
How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]
- Md. Shad Akhtar, Asif Ekbal, E. Cambria
- Computer ScienceIEEE Computational Intelligence Magazine
- 10 January 2020
A stacked ensemble method for predicting the degree of intensity for emotion and sentiment by combining the outputs obtained from several deep learning and classical feature-based models using a multi-layer perceptron network is proposed.
Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach
Results show that the vote based classifier ensemble identified by the GA-based approach outperforms all the individual classifiers, three conventional baseline ensembles, and some other existing ensemble techniques.
Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts
The findings of the shared tasks conducted at the CONSTRAINT Workshop at AAAI 2021 are presented and the most successful models were BERT or its variations.