What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Natural language processing
His primary areas of study are Artificial intelligence, Natural language processing, Machine translation, Syntax and Translation.
His biological study spans a wide range of topics, including Linguistics, String and Theoretical computer science.
His Natural language processing research incorporates themes from Speech recognition and Word.
Kevin Knight combines subjects such as Transduction, Noun phrase and Phrase with his study of Machine translation.
As a part of the same scientific family, Kevin Knight mostly works in the field of Syntax, focusing on Parse tree and, on occasion, Data mining.
His biological study spans a wide range of topics, including Named entity, Context and Entity linking.
His most cited work include:
- A Syntax-based Statistical Translation Model (772 citations)
- Abstract Meaning Representation for Sembanking (747 citations)
- What’s in a translation rule? (475 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Natural language processing, Machine translation, Speech recognition and Translation are his primary areas of study.
In most of his Artificial intelligence studies, his work intersects topics such as Machine learning.
His Natural language processing research is multidisciplinary, relying on both Linguistics and String.
His biological study deals with issues like Tree, which deal with fields such as Theoretical computer science.
Machine translation connects with themes related to Rule-based machine translation in his study.
The concepts of his Word study are interwoven with issues in Language model and Decoding methods.
He most often published in these fields:
- Artificial intelligence (66.79%)
- Natural language processing (60.36%)
- Machine translation (40.00%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.79%)
- Natural language processing (60.36%)
- Machine translation (40.00%)
In recent papers he was focusing on the following fields of study:
Kevin Knight focuses on Artificial intelligence, Natural language processing, Machine translation, Information retrieval and Natural language.
Many of his research projects under Artificial intelligence are closely connected to Resource with Resource, tying the diverse disciplines of science together.
His research in Natural language processing intersects with topics in Translation, Word and Pinyin.
His Machine translation research incorporates themes from Sentence, Language model, Speech recognition and Computational linguistics.
His work on Patent document and Knowledge graph as part of general Information retrieval study is frequently connected to Subject-matter expert and Hull, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Natural language study incorporates themes from Human–computer interaction, Storytelling and Knowledge base.
Between 2017 and 2021, his most popular works were:
- Towards Controllable Story Generation (66 citations)
- FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN. (49 citations)
- Plan-And-Write: Towards Better Automatic Storytelling (47 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Programming language
His main research concerns Artificial intelligence, Natural language processing, Recurrent neural network, Annotation and Human–computer interaction.
He incorporates Artificial intelligence and Localization system in his research.
His studies deal with areas such as Bridging and Coreference as well as Natural language processing.
His Recurrent neural network study integrates concerns from other disciplines, such as Equivalence, Theoretical computer science, Computational complexity theory, Softmax function and Phrase.
He works mostly in the field of Human–computer interaction, limiting it down to concerns involving Storytelling and, occasionally, Natural language.
His Word research includes elements of Similarity, BLEU, Machine translation and Suffix.
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