H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,920 78 World Ranking 8501 National Ranking 3945

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Natural language processing
  • Machine learning

His main research concerns Artificial intelligence, Natural language processing, Parsing, Top-down parsing and Parser combinator. His Interpretation research extends to the thematically linked field of Artificial intelligence. His Natural language processing research includes themes of Context awareness, Pragmatics, Affective computing, Visualization and Feature extraction.

Kenji Sagae combines subjects such as Representation, Component and Measure with his study of Parsing. His study in LR parser and Top-down parsing language is done as part of Top-down parsing. His Canonical LR parser study deals with Simple LR parser intersecting with Speech recognition.

His most cited work include:

  • Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles (233 citations)
  • Dynamic Programming for Linear-Time Incremental Parsing (213 citations)
  • YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context (191 citations)

What are the main themes of his work throughout his whole career to date?

Kenji Sagae mainly focuses on Artificial intelligence, Natural language processing, Parsing, Speech recognition and Top-down parsing. In Artificial intelligence, Kenji Sagae works on issues like Task, which are connected to Isolation. The concepts of his Natural language processing study are interwoven with issues in Dependency and Domain.

His study looks at the relationship between Parsing and topics such as CHILDES, which overlap with Annotation, Ambiguity and Syntax. His work carried out in the field of Speech recognition brings together such families of science as Adaptation and Machine translation. His research is interdisciplinary, bridging the disciplines of Parser combinator and Top-down parsing.

He most often published in these fields:

  • Artificial intelligence (61.22%)
  • Natural language processing (54.08%)
  • Parsing (34.69%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (61.22%)
  • Natural language processing (54.08%)
  • Context (10.20%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Natural language processing, Context, Natural language and Programming language are his primary areas of study. He interconnects Competence, Field, Spanish language and Written language in the investigation of issues within Artificial intelligence. He works on Natural language processing which deals in particular with Parsing.

Kenji Sagae has included themes like Second language, Language understanding and Heritage language in his Context study. His study looks at the relationship between Natural language and fields such as Set, as well as how they intersect with chemical problems. His research in Programming language intersects with topics in Argument, Grammar, Syntax and Reading.

Between 2017 and 2021, his most popular works were:

  • Developing NLP Tools with a New Corpus of Learner Spanish (4 citations)
  • Studying the difference between natural and programming language corpora (4 citations)
  • UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder (3 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Natural language processing
  • Machine learning

His primary scientific interests are in Natural language processing, Artificial intelligence, Field, Spanish language and Encoder. His Encoder studies intersect with other subjects such as Feature engineering, Parsing, Graph and SemEval.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles

Kenji Sagae;Jun'ichi Tsujii.
empirical methods in natural language processing (2007)

255 Citations

Dynamic Programming for Linear-Time Incremental Parsing

Liang Huang;Kenji Sagae.
meeting of the association for computational linguistics (2010)

249 Citations

YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context

M. Wollmer;F. Weninger;T. Knaup;B. Schuller.
IEEE Intelligent Systems (2013)

248 Citations

Parser Combination by Reparsing

Kenji Sagae;Alon Lavie.
north american chapter of the association for computational linguistics (2006)

201 Citations

Evaluating contributions of natural language parsers to protein–protein interaction extraction

Yusuke Miyao;Kenji Sagae;Rune Sætre;Takuya Matsuzaki.
Bioinformatics (2009)

188 Citations

A Classifier-Based Parser with Linear Run-Time Complexity

Kenji Sagae;Alon Lavie.
international workshop/conference on parsing technologies (2005)

150 Citations

The significance of recall in automatic metrics for MT evaluation

Alon Lavie;Kenji Sagae;Shyamsundar Jayaraman.
conference of the association for machine translation in the americas (2004)

138 Citations

Syntactic Features for Protein-Protein Interaction Extraction.

Rune Sætre;Kenji Sagae;Jun'ichi Tsujii.
LBM (Short Papers) (2007)

124 Citations

Task-oriented Evaluation of Syntactic Parsers and Their Representations

Yusuke Miyao;Rune Saetre;Kenji Sagae;Takuya Matsuzaki.
meeting of the association for computational linguistics (2008)

123 Citations

Incremental interpretation and prediction of utterance meaning for interactive dialogue

David DeVault;Kenji Sagae;David R. Traum.
Dialogue & Discourse (2011)

111 Citations

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