What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Natural language processing, Sentiment analysis, Natural language and Machine learning.
As part of his studies on Artificial intelligence, Alexander Gelbukh often connects relevant subjects like Pattern recognition.
His biological study spans a wide range of topics, including Similarity, The Internet and Information retrieval.
His studies deal with areas such as Intelligent decision support system, Field, Affective computing and State as well as Sentiment analysis.
His studies in Natural language integrate themes in fields like Cluster analysis, Knowledge representation and reasoning, Text mining, Semantics and On Language.
His Computational linguistics research is multidisciplinary, relying on both Question answering, Human–computer information retrieval and Biomedical text mining.
His most cited work include:
- Computational Linguistics and Intelligent Text Processing (784 citations)
- Aspect extraction for opinion mining with a deep convolutional neural network (455 citations)
- Deep Learning-Based Document Modeling for Personality Detection from Text (289 citations)
What are the main themes of his work throughout his whole career to date?
Alexander Gelbukh mainly investigates Artificial intelligence, Natural language processing, Information retrieval, Natural language and Word.
The concepts of his Artificial intelligence study are interwoven with issues in Context and Machine learning.
Alexander Gelbukh has researched Natural language processing in several fields, including Semantics and Word-sense disambiguation.
His study in Document retrieval, Question answering and Search engine indexing is done as part of Information retrieval.
His Natural language research includes elements of Ontology and Text processing.
In his research, Alexander Gelbukh performs multidisciplinary study on Sentiment analysis and Polarity.
He most often published in these fields:
- Artificial intelligence (73.59%)
- Natural language processing (62.90%)
- Information retrieval (17.34%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (73.59%)
- Natural language processing (62.90%)
- Sentiment analysis (10.08%)
In recent papers he was focusing on the following fields of study:
Alexander Gelbukh focuses on Artificial intelligence, Natural language processing, Sentiment analysis, Deep learning and Convolutional neural network.
He interconnects Context, Machine learning and Identification in the investigation of issues within Artificial intelligence.
His work deals with themes such as Test, Word, Word embedding, Textual entailment and Code, which intersect with Natural language processing.
His research integrates issues of Social media, State, The Internet and Classifier in his study of Sentiment analysis.
The various areas that Alexander Gelbukh examines in his Deep learning study include Class and Data science.
His work in Convolutional neural network addresses subjects such as Test set, which are connected to disciplines such as Code-switching and SemEval.
Between 2016 and 2021, his most popular works were:
- Deep Learning-Based Document Modeling for Personality Detection from Text (289 citations)
- Sentiment Analysis Is a Big Suitcase (169 citations)
- DialogueRNN: An Attentive RNN for Emotion Detection in Conversations. (116 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Machine learning
His primary areas of study are Artificial intelligence, Natural language processing, Sentiment analysis, Convolutional neural network and Machine learning.
He has included themes like Field and Conversation in his Artificial intelligence study.
His Natural language processing study incorporates themes from Hybrid approach, Stop words, Textual entailment, Simple and Benchmark.
His Sentiment analysis research incorporates themes from Word, State, Affective computing, Adaptation and Social media.
In his research, Information retrieval is intimately related to Relation, which falls under the overarching field of State.
His work carried out in the field of Machine learning brings together such families of science as Rank and Identification.
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