What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
His primary areas of investigation include Artificial intelligence, Natural language processing, Parsing, Task and Head-driven phrase structure grammar.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
Yusuke Miyao combines subjects such as Generative grammar, Support vector machine and Task with his study of Natural language processing.
Yusuke Miyao has researched Parsing in several fields, including Sentence, Probabilistic logic and Rule-based machine translation.
His research in Task intersects with topics in Paraphrase, Logical consequence, Inference and Contradiction.
Yusuke Miyao focuses mostly in the field of Bottom-up parsing, narrowing it down to topics relating to Ambiguity and, in certain cases, Domain and Information extraction.
His most cited work include:
- Probabilistic CFG with Latent Annotations (253 citations)
- Event extraction from biomedical papers using a full parser. (218 citations)
- Feature forest models for probabilistic hpsg parsing (183 citations)
What are the main themes of his work throughout his whole career to date?
Yusuke Miyao mostly deals with Artificial intelligence, Natural language processing, Parsing, Head-driven phrase structure grammar and Treebank.
His research on Artificial intelligence often connects related areas such as Task.
The Task study combines topics in areas such as Question answering and Inference.
His studies in Natural language processing integrate themes in fields like Annotation, Programming language and Grammar.
His Parsing research also works with subjects such as
- Probabilistic logic which is related to area like Statistical model,
- Information retrieval together with Relation.
His research integrates issues of Vietnamese and Text segmentation in his study of Treebank.
He most often published in these fields:
- Artificial intelligence (85.71%)
- Natural language processing (81.43%)
- Parsing (41.43%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (85.71%)
- Natural language processing (81.43%)
- Treebank (16.67%)
In recent papers he was focusing on the following fields of study:
Yusuke Miyao mainly focuses on Artificial intelligence, Natural language processing, Treebank, Parsing and Task.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Baseline and other disciplines.
His work carried out in the field of Natural language processing brings together such families of science as Annotation, Textual entailment and Relation.
His Treebank research is multidisciplinary, incorporating perspectives in Syntactic structure, Scheme and Vietnamese.
His Parsing research is multidisciplinary, incorporating elements of Example-based machine translation, Machine translation software usability, Word order and Graph.
His Task research incorporates elements of Context and Closed captioning.
Between 2015 and 2021, his most popular works were:
- Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths (47 citations)
- Using Left-corner Parsing to Encode Universal Structural Constraints in Grammar Induction (34 citations)
- TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations. (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Natural language processing
Yusuke Miyao focuses on Artificial intelligence, Natural language processing, Econometrics, Textual entailment and Primary market.
His studies deal with areas such as Machine learning and Task as well as Artificial intelligence.
His study on Treebank is often connected to On demand as part of broader study in Natural language processing.
His Textual entailment study combines topics from a wide range of disciplines, such as Lexical knowledge, Automated theorem proving and Relation.
His Primary market study combines topics in areas such as Stock market bubble and Restricted stock.
Many of his research projects under Programming language are closely connected to ENCODE with ENCODE, tying the diverse disciplines of science together.
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