What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary scientific interests are in Artificial intelligence, Natural language processing, Named-entity recognition, Bengali and Support vector machine.
Asif Ekbal interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
Asif Ekbal usually deals with Natural language processing and limits it to topics linked to Word and Context.
His Named-entity recognition research integrates issues from Class, Identifier, Text corpus and Annotation.
Asif Ekbal has included themes like Variety, Cross-validation, Speech recognition, Entity linking and Machine translation in his Bengali study.
The study incorporates disciplines such as Sentence, Information retrieval and Decision tree in addition to Support vector machine.
His most cited work include:
- The CHEMDNER corpus of chemicals and drugs and its annotation principles. (220 citations)
- Bengali Named Entity Recognition Using Support Vector Machine (80 citations)
- Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition (79 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Natural language processing, Machine learning, Pattern recognition and Named-entity recognition.
His is doing research in Conditional random field, Bengali, Support vector machine, Classifier and Deep learning, both of which are found in Artificial intelligence.
The various areas that Asif Ekbal examines in his Natural language processing study include Speech recognition and Word.
His Machine learning research is multidisciplinary, incorporating elements of Feature extraction and Benchmark.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Multi-objective optimization, Data mining and Cluster analysis, Fuzzy clustering.
His research in Named-entity recognition intersects with topics in Active learning, Variety, Principle of maximum entropy, Named entity and Information extraction.
He most often published in these fields:
- Artificial intelligence (82.42%)
- Natural language processing (44.85%)
- Machine learning (25.45%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.42%)
- Natural language processing (44.85%)
- Deep learning (13.33%)
In recent papers he was focusing on the following fields of study:
Asif Ekbal mostly deals with Artificial intelligence, Natural language processing, Deep learning, Multi-task learning and Machine learning.
His Artificial intelligence study focuses mostly on Sentence, Sentiment analysis, Benchmark, Question answering and Leverage.
His work on Hindi and Machine translation as part of general Natural language processing research is often related to Offensive, thus linking different fields of science.
His Deep learning research incorporates themes from Classifier, Paragraph, Recurrent neural network and BLEU.
His Multi-task learning research incorporates elements of Dependency, The Internet and Voice activity detection.
Many of his research projects under Machine learning are closely connected to Pipeline with Pipeline, tying the diverse disciplines of science together.
Between 2019 and 2021, his most popular works were:
- How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes] (61 citations)
- Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts (34 citations)
- Fighting an Infodemic: COVID-19 Fake News Dataset. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
His scientific interests lie mostly in Artificial intelligence, Deep learning, Natural language processing, Multi-task learning and Machine learning.
Asif Ekbal connects Artificial intelligence with Field in his study.
His Deep learning research includes elements of Clef and Spoken language.
His work on Hindi as part of his general Natural language processing study is frequently connected to Offensive, thereby bridging the divide between different branches of science.
His Machine learning study incorporates themes from Dependency and Benchmark.
His Benchmark research includes themes of Decision tree and Information retrieval.
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