What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Xiaodan Zhu focuses on Artificial intelligence, Natural language processing, Sentiment analysis, Lexicon and Inference.
His study in Leverage and Natural language understanding falls under the purview of Artificial intelligence.
His work in Natural language processing addresses issues such as Test set, which are connected to fields such as Overfitting.
The various areas that Xiaodan Zhu examines in his Sentiment analysis study include Crowdsourcing and Data mining.
Xiaodan Zhu combines subjects such as Variety, State and Support vector machine with his study of Lexicon.
His Inference research is multidisciplinary, incorporating elements of Artificial neural network and Machine learning.
His most cited work include:
- Enhanced LSTM for Natural Language Inference (607 citations)
- Sentiment analysis of short informal texts (554 citations)
- NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets (409 citations)
What are the main themes of his work throughout his whole career to date?
Xiaodan Zhu mainly investigates Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Inference.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Context.
His Natural language processing study combines topics from a wide range of disciplines, such as Variety, SemEval and Test set.
His Machine learning research integrates issues from Representation, Stance detection and Redundancy.
His studies examine the connections between Artificial neural network and genetics, as well as such issues in Adaptation, with regards to Question answering.
His Inference research is multidisciplinary, relying on both Attention network and Benchmark.
He most often published in these fields:
- Artificial intelligence (80.20%)
- Natural language processing (46.53%)
- Machine learning (23.76%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.20%)
- Natural language processing (46.53%)
- Natural language (13.86%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Natural language processing, Natural language, Machine learning and Selection.
His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence.
Many of his research projects under Natural language processing are closely connected to Closed captioning with Closed captioning, tying the diverse disciplines of science together.
Statement and Sentence is closely connected to SemEval in his research, which is encompassed under the umbrella topic of Natural language.
His studies deal with areas such as Language model, Context, Persona and Information retrieval as well as Selection.
His work investigates the relationship between Information retrieval and topics such as Dialog box that intersect with problems in Latent variable.
Between 2019 and 2021, his most popular works were:
- SemEval-2020 Task 4: Commonsense Validation and Explanation (43 citations)
- Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots (12 citations)
- Learning Cross-Modal Context Graph for Visual Grounding (8 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, Natural language processing, Language model, Leverage and Closed captioning.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Dialog box.
His work carried out in the field of Machine learning brings together such families of science as Attention network and Inference.
His work in the fields of Natural language processing, such as Natural language, intersects with other areas such as Statement and Key.
The Language model study combines topics in areas such as Context, Adaptation, Human–computer interaction and Selection.
He integrates many fields in his works, including Context, Small number and Domain adaptation.
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