D-Index & Metrics Best Publications
Computer Science
Japan
2022
Computer Science
UK
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 66 Citations 18,214 408 World Ranking 1458 National Ranking 84

Research.com Recognitions

Awards & Achievements

2023 - Research.com Computer Science in United Kingdom Leader Award

2022 - Research.com Computer Science in Japan Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Natural language processing
  • Programming language

His primary areas of study are Artificial intelligence, Natural language processing, Parsing, Information retrieval and Biomedical text mining. His studies deal with areas such as Machine learning, Task and Pattern recognition as well as Artificial intelligence. His Natural language processing study incorporates themes from Domain and Probabilistic logic.

Jun'ichi Tsujii has included themes like Object, Syntax, Natural language and Component in his Parsing study. His Information retrieval study integrates concerns from other disciplines, such as Text mining, Web application, Annotation and The Internet. His Biomedical text mining research is multidisciplinary, incorporating elements of Event and Distributed computing.

His most cited work include:

  • GENIA corpus—a semantically annotated corpus for bio-textmining (798 citations)
  • brat: a Web-based Tool for NLP-Assisted Text Annotation (619 citations)
  • Overview of BioNLP'09 Shared Task on Event Extraction (501 citations)

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

His scientific interests lie mostly in Artificial intelligence, Natural language processing, Parsing, Information retrieval and Task. His Artificial intelligence research incorporates themes from Machine learning, Speech recognition and Biomedical text mining. His Natural language processing study combines topics in areas such as Domain, Annotation and Grammar.

His research integrates issues of Syntax and Rule-based machine translation in his study of Parsing. His work carried out in the field of Information retrieval brings together such families of science as Text mining and Named-entity recognition. In his work, S-attributed grammar and Parsing expression grammar is strongly intertwined with Parser combinator, which is a subfield of Top-down parsing.

He most often published in these fields:

  • Artificial intelligence (82.31%)
  • Natural language processing (73.70%)
  • Parsing (25.40%)

What were the highlights of his more recent work (between 2010-2021)?

  • Artificial intelligence (82.31%)
  • Natural language processing (73.70%)
  • Task (19.05%)

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

Jun'ichi Tsujii mostly deals with Artificial intelligence, Natural language processing, Task, Biomedical text mining and Information retrieval. The study incorporates disciplines such as Named-entity recognition and Data mining in addition to Artificial intelligence. He combines subjects such as Domain, Head-driven phrase structure grammar and Coreference with his study of Natural language processing.

His studies deal with areas such as Annotation and Ontology as well as Task. His research integrates issues of Class, Information extraction, Set, Event and Software in his study of Biomedical text mining. His research on Information retrieval also deals with topics like

  • Text mining that intertwine with fields like Representation,
  • Test data, which have a strong connection to Task analysis.

Between 2010 and 2021, his most popular works were:

  • brat: a Web-based Tool for NLP-Assisted Text Annotation (619 citations)
  • Overview of BioNLP Shared Task 2011 (194 citations)
  • Event extraction across multiple levels of biological organization (89 citations)

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

  • Artificial intelligence
  • Programming language
  • Natural language processing

The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Task, Biomedical text mining and Event. His Artificial intelligence research incorporates themes from Named-entity recognition, Data mining and Pattern recognition. His work carried out in the field of Natural language processing brings together such families of science as Class, Joint and Text segmentation.

His biological study spans a wide range of topics, including Domain and Information retrieval. His study in Information retrieval is interdisciplinary in nature, drawing from both Text mining, Web application and Annotation. Jun'ichi Tsujii interconnects Data science, Coreference and Set in the investigation of issues within Biomedical text mining.

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

GENIA corpus—a semantically annotated corpus for bio-textmining

Jin-Dong Kim;Tomoko Ohta;Yuka Tateisi;Jun'ichi Tsujii.
Bioinformatics (2003)

1317 Citations

brat: a Web-based Tool for NLP-Assisted Text Annotation

Pontus Stenetorp;Sampo Pyysalo;Goran Topić;Tomoko Ohta.
conference of the european chapter of the association for computational linguistics (2012)

1104 Citations

Overview of BioNLP'09 Shared Task on Event Extraction

Jin-Dong Kim;Tomoko Ohta;Sampo Pyysalo;Yoshinobu Kano.
north american chapter of the association for computational linguistics (2009)

757 Citations

Developing a robust part-of-speech tagger for biomedical text

Yoshimasa Tsuruoka;Yuka Tateishi;Jin-Dong Kim;Tomoko Ohta.
panhellenic conference on informatics (2005)

622 Citations

Corpus annotation for mining biomedical events from literature

Jin Dong Kim;Tomoko Ohta;Jun'ichi Tsujii;Jun'ichi Tsujii.
BMC Bioinformatics (2008)

451 Citations

Text mining and its potential applications in systems biology

Sophia Ananiadou;Douglas B. Kell;Jun ichi Tsujii;Jun ichi Tsujii.
Trends in Biotechnology (2006)

411 Citations

Accomplishments and challenges in literature data mining for biology

Lynette Hirschman;Jong C. Park;Junichi Tsujii;Limsoon Wong.
Bioinformatics (2002)

403 Citations

Extracting the names of genes and gene products with a hidden Markov model

Nigel Collier;Chikashi Nobata;Jun-ichi Tsujii.
international conference on computational linguistics (2000)

373 Citations

Tuning support vector machines for biomedical named entity recognition

Jun'ichi Kazama;Takaki Makino;Yoshihiro Ohta;Jun'ichi Tsujii.
meeting of the association for computational linguistics (2002)

364 Citations

Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data

Yoshimasa Tsuruoka;Jun'ichi Tsujii.
empirical methods in natural language processing (2005)

329 Citations

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