H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 63 Citations 21,490 126 World Ranking 1280 National Ranking 731

Research.com Recognitions

Awards & Achievements

1990 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)

Overview

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 study are Artificial intelligence, Natural language processing, Parsing, Speech recognition and Top-down parsing. His study connects Machine learning and Artificial intelligence. His Natural language processing study incorporates themes from Set, Semantic lexicon and Information retrieval.

The Parsing study combines topics in areas such as Sentence, Discriminative model and Word error rate. As part of one scientific family, he deals mainly with the area of Speech recognition, narrowing it down to issues related to the Self training, and often Bootstrapping. His Top-down parsing research includes themes of S-attributed grammar and Parser combinator.

His most cited work include:

  • A maximum-entropy-inspired parser (1446 citations)
  • Introduction to Artificial Intelligence (1043 citations)
  • Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking (866 citations)

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

Eugene Charniak mostly deals with Artificial intelligence, Natural language processing, Parsing, Speech recognition and Natural language. Eugene Charniak has researched Artificial intelligence in several fields, including Machine learning and Set. His Natural language processing study frequently draws parallels with other fields, such as Coreference.

His study in Top-down parsing, Statistical parsing, Treebank, Bottom-up parsing and LR parser is carried out as part of his studies in Parsing. His work in Top-down parsing covers topics such as Parser combinator which are related to areas like Top-down parsing language. He combines subjects such as Bigram, Word and Self training with his study of Speech recognition.

He most often published in these fields:

  • Artificial intelligence (70.19%)
  • Natural language processing (56.52%)
  • Parsing (30.43%)

What were the highlights of his more recent work (between 2008-2020)?

  • Artificial intelligence (70.19%)
  • Natural language processing (56.52%)
  • Information retrieval (6.83%)

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

Artificial intelligence, Natural language processing, Information retrieval, Parsing and Programming language are his primary areas of study. Eugene Charniak has included themes like Machine learning and Speech recognition in his Artificial intelligence study. His research on Natural language processing focuses in particular on Syntax.

His work on Automatic summarization and Query expansion as part of general Information retrieval research is frequently linked to Concept search and Information needs, thereby connecting diverse disciplines of science. His study in Bottom-up parsing and Top-down parsing are all subfields of Parsing. His work on Macro, Data-driven programming, Coroutine and Data structure as part of general Programming language research is frequently linked to Order, bridging the gap between disciplines.

Between 2008 and 2020, his most popular works were:

  • Automatic Domain Adaptation for Parsing (151 citations)
  • Any domain parsing: automatic domain adaptation for natural language parsing (127 citations)
  • Parsing as Language Modeling (103 citations)

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

  • Artificial intelligence
  • Programming language
  • Natural language processing

His primary scientific interests are in Artificial intelligence, Natural language processing, Context, Domain and S-attributed grammar. His Artificial intelligence research incorporates themes from Set and Pattern recognition. He studies Named entity which is a part of Natural language processing.

His study on Context also encompasses disciplines like

  • Irony together with Data mining, Word, Feature vector and Noun phrase,
  • Sarcasm which connect with Syntax. The concepts of his Domain study are interwoven with issues in Topic model, Adaptation, Coherence, Machine learning and Target text. His S-attributed grammar research is under the purview of Parsing.

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.

Top Publications

Introduction to Artificial Intelligence

Eugene Charniak;Drew McDermott.
(1985)

2534 Citations

A maximum-entropy-inspired parser

Eugene Charniak.
north american chapter of the association for computational linguistics (2000)

2271 Citations

Statistical Language Learning

Eugene Charniak.
(1994)

1726 Citations

Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking

Eugene Charniak;Mark Johnson.
meeting of the association for computational linguistics (2005)

1348 Citations

Statistical parsing with a context-free grammar and word statistics

Eugene Charniak.
national conference on artificial intelligence (1997)

760 Citations

Finding Parts in Very Large Corpora

Matthew Berland;Eugene Charniak.
meeting of the association for computational linguistics (1999)

714 Citations

A Bayesian model of plan recognition

Eugene Charniak;Robert P. Goldman.
Artificial Intelligence (1993)

627 Citations

Effective Self-Training for Parsing

David McClosky;Eugene Charniak;Mark Johnson.
language and technology conference (2006)

602 Citations

Artificial Intelligence Programming

Eugene Charniak;James R. Meehan;Christopher K. Reisbeck;Drew V. McDermott.
(1980)

529 Citations

Immediate-Head Parsing for Language Models

Eugene Charniak.
meeting of the association for computational linguistics (2001)

456 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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