Eugene Charniak

133 papers receiving 7.0k citations

Hit Papers

A Maximum-Entropy-Inspired Parser199420262004201519992005199420062505007501000

Peers

Eugene Charniak
Comparison fields: 5 of 130
  • Artificial Intelligence 7.4k
  • Molecular Biology 715
  • Computer Vision and Pattern Recognition 608
  • Information Systems 568
  • Language and Linguistics 290
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Citations per year

Countries citing papers authored by Eugene Charniak

Since Specialization
Citations

This map shows the geographic impact of Eugene Charniak's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eugene Charniak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Charniak more than expected).

Fields of papers citing papers by Eugene Charniak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eugene Charniak. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eugene Charniak. The network helps show where Eugene Charniak may publish in the future.

Co-authorship network of co-authors of Eugene Charniak

This figure shows the co-authorship network connecting the top 25 collaborators of Eugene Charniak. A scholar is included among the top collaborators of Eugene Charniak based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eugene Charniak. Eugene Charniak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
A Context Free TAG Variant
2
2
$S^3$ - Statistical Sandhi Splitting
5
3
Top-Down Nearly-Context-Sensitive Parsing
6
4
Using the Penn Treebank to Evaluate Non-Treebank Parsers
9
5
Assigning function tags to parsed text
73
6
A Maximum-Entropy-Inspired Parserbreakdown →
1036
7
A Statistical Approach to Anaphora Resolution
148
8 104
9
Figures of Merit for Best-First Probabilistic Chart Parsing
7
10
Tree-bank Grammars
164
11
Context-sensitive statistics for improved grammatical language models
21
12
Equations for part-of-speech tagging
118
13
Dynamic MAP calculations for abduction
12
14 14
15
A semantics for probabilistic quantifier-free first-order languages, with particular application to story understanding
61
16 116
17 90
18
Six topics in search of a parser: an overview of AI language research
7
19
Ms. maloprop, a language comprehension program
18
20
A partial taxonomy of knowledge about actions
10

About Eugene Charniak

Eugene Charniak is a scholar working on Artificial Intelligence, Language and Linguistics and Architecture, having authored 135 papers that have together received 8.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (91 papers), Topic Modeling (77 papers) and Speech and dialogue systems (37 papers). The work is most often cited by research in Artificial Intelligence (7.4k citations), Computer Vision and Pattern Recognition (608 citations) and Language and Linguistics (290 citations). Eugene Charniak has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Mark Johnson, David McClosky, Robert P. Goldman, Micha Elsner, Matthew Berland, SW, Dmitriy Genzel, Glenn R. Carroll, Brian Roark and Solomon Eyal Shimony. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Behavioral and Brain Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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