D-Index & Metrics Best Publications

Stephan Oepen

University of Oslo
Norway

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 Citations Publications World Ranking National Ranking

Computer Science D-index 33 Citations 3,743 117 World Ranking 8802 National Ranking 22

Overview

What is he best known for?

The fields of study Stephan Oepen is best known for:

Finance
Politics
Linguistics

His Linguistics study frequently intersects with other fields, such as Grammar and Word (group theory). Word (group theory) is closely attributed to Linguistics in his work. His Artificial intelligence study frequently draws connections between related disciplines such as Selection (genetic algorithm). His study brings together the fields of Artificial intelligence and Selection (genetic algorithm). He integrates Natural language processing with Rule-based machine translation in his research. He combines topics linked to Construct (python library) with his work on Programming language. Stephan Oepen conducts interdisciplinary study in the fields of Parsing and Treebank through his works. He performs integrative Treebank and Parsing research in his work. Many of his studies on Set (abstract data type) apply to Programming language as well.

His most cited work include:

LinGO Redwoods (119 citations)
Discriminant-Based MRS Banking (78 citations)
Word vectors, reuse, and replicability: Towards a community repository of large-text resources (76 citations)

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

Stephan Oepen merges Natural language processing with Annotation in his research. His multidisciplinary approach integrates Annotation and Linguistics in his work. While working in this field, he studies both Linguistics and Grammar. Stephan Oepen incorporates Artificial intelligence and Theoretical computer science in his studies. Parsing and Treebank are two areas of study in which Stephan Oepen engages in interdisciplinary work. In his works, Stephan Oepen conducts interdisciplinary research on Treebank and Parsing. His work often combines Programming language and Semantics (computer science) studies. He integrates Semantics (computer science) with Programming language in his research. His work in Sentence is not limited to one particular discipline; it also encompasses Semantic role labeling.

Stephan Oepen most often published in these fields:

Natural language processing (92.86%)
Artificial intelligence (89.29%)
Linguistics (67.86%)

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

Law (100.00%)
Linguistics (100.00%)
Politics (100.00%)

In recent works Stephan Oepen was focusing on the following fields of study:

Stephan Oepen integrates Law with Parliament in his research. Stephan Oepen conducts interdisciplinary study in the fields of Parliament and Law through his works. As part of his studies on Linguistics, he often connects relevant areas like Norwegian. He combines Politics and Representation (politics) in his research. He merges Representation (politics) with Politics in his research. Stephan Oepen merges many fields, such as Semantics (computer science) and Programming language, in his writings. His multidisciplinary approach integrates Programming language and Semantics (computer science) in his work. He performs multidisciplinary study in the fields of Artificial intelligence and Natural language processing via his papers. Much of his study explores Natural language processing relationship to Sentence.

Between 2017 and 2021, his most popular works were:

The Talk of Norway: a richly annotated corpus of the Norwegian parliament, 1998–2016 (19 citations)
Designing a Uniform Meaning Representation for Natural Language Processing (10 citations)

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

The grammar matrix: an open-source starter-kit for the rapid development of cross-linguistically consistent broad-coverage precision grammars

Emily M. Bender;Dan Flickinger;Stephan Oepen.
international conference on computational linguistics (2002)

345 Citations

SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing

Stephan Oepen;Marco Kuhlmann;Yusuke Miyao;Daniel Zeman.
north american chapter of the association for computational linguistics (2014)

205 Citations

LinGO Redwoods: A Rich and Dynamic Treebank for HPSG

Stephan Oepen;Dan Flickinger;Kristina Toutanova;Christopher D. Manning.
Research on Language and Computation (2004)

204 Citations

The LinGO Redwoods treebank motivation and preliminary applications

Stephan Oepen;Kristina Toutanova;Stuart Shieber;Christopher Manning.
international conference on computational linguistics (2002)

167 Citations

TSNLP: Test Suites for Natural Language Processing

Sabine Lehmann;Stephan Oepen;Sylvie Regnier-Prost;Klaus Netter.
international conference on computational linguistics (1996)

134 Citations

Word vectors, reuse, and replicability: Towards a community repository of large-text resources

Murhaf Fares;Andrey Kutuzov;Stephan Oepen;Erik Velldal.
Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden (2017)

130 Citations

Discriminant-Based MRS Banking

Stephan Oepen;Jan Tore Lønning.
language resources and evaluation (2006)

122 Citations

High efficiency realization for a wide-coverage unification grammar

John Carroll;Stephan Oepen.
(2005)

113 Citations

Towards systematic grammar profiling.Test suite technology 10 years after

Stephan Oepen;Dan Flickinger.
Computer Speech & Language (1998)

101 Citations

Stochastic HPSG Parse Disambiguation using the Redwoods Corpus

Kristina Toutanova;Christopher D. Manning;Dan Flickinger;Stephan Oepen.
Research on Language and Computation (2005)