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The UCREL semantic analysis system.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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The UCREL semantic analysis system. / Rayson, P.; Archer, Dawn; Piao, S. et al.
Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004. Lisbon, 2004. p. 7-12.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Rayson, P, Archer, D, Piao, S & McEnery, AM 2004, The UCREL semantic analysis system. in Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004. Lisbon, pp. 7-12, Beyond Named Entity Recognition Semantic labelling for NLP tasks , Lisbon, Portugal, 25/05/04.

APA

Rayson, P., Archer, D., Piao, S., & McEnery, A. M. (2004). The UCREL semantic analysis system. In Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004 (pp. 7-12).

Vancouver

Rayson P, Archer D, Piao S, McEnery AM. The UCREL semantic analysis system. In Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004. Lisbon. 2004. p. 7-12

Author

Rayson, P. ; Archer, Dawn ; Piao, S. et al. / The UCREL semantic analysis system. Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004. Lisbon, 2004. pp. 7-12

Bibtex

@inproceedings{2edc9492ab1f42dfaf26ecd49b11c1b1,
title = "The UCREL semantic analysis system.",
abstract = "The UCREL semantic analysis system (USAS) is a software tool for undertaking the automatic semantic analysis of English spoken and written data. This paper describes the software system, and the hierarchical semantic tag set containing 21 major discourse fields and 232 fine-grained semantic field tags. We discuss the manually constructed lexical resources on which the system relies, and the seven disambiguation methods including part-of-speech tagging, general likelihood ranking, multi-word-expression extraction, domain of discourse identification, and contextual rules. We report an evaluation of the accuracy of the system compared to a manually tagged test corpus on which the USAS software obtained a precision value of 91%. Finally, we make reference to the applications of the system in corpus linguistics, content analysis, software engineering, and electronic dictionaries",
author = "P. Rayson and Dawn Archer and S. Piao and McEnery, {A. M.}",
year = "2004",
language = "English",
pages = "7--12",
booktitle = "Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004",
note = "Beyond Named Entity Recognition Semantic labelling for NLP tasks ; Conference date: 25-05-2004 Through 25-05-2004",

}

RIS

TY - GEN

T1 - The UCREL semantic analysis system.

AU - Rayson, P.

AU - Archer, Dawn

AU - Piao, S.

AU - McEnery, A. M.

PY - 2004

Y1 - 2004

N2 - The UCREL semantic analysis system (USAS) is a software tool for undertaking the automatic semantic analysis of English spoken and written data. This paper describes the software system, and the hierarchical semantic tag set containing 21 major discourse fields and 232 fine-grained semantic field tags. We discuss the manually constructed lexical resources on which the system relies, and the seven disambiguation methods including part-of-speech tagging, general likelihood ranking, multi-word-expression extraction, domain of discourse identification, and contextual rules. We report an evaluation of the accuracy of the system compared to a manually tagged test corpus on which the USAS software obtained a precision value of 91%. Finally, we make reference to the applications of the system in corpus linguistics, content analysis, software engineering, and electronic dictionaries

AB - The UCREL semantic analysis system (USAS) is a software tool for undertaking the automatic semantic analysis of English spoken and written data. This paper describes the software system, and the hierarchical semantic tag set containing 21 major discourse fields and 232 fine-grained semantic field tags. We discuss the manually constructed lexical resources on which the system relies, and the seven disambiguation methods including part-of-speech tagging, general likelihood ranking, multi-word-expression extraction, domain of discourse identification, and contextual rules. We report an evaluation of the accuracy of the system compared to a manually tagged test corpus on which the USAS software obtained a precision value of 91%. Finally, we make reference to the applications of the system in corpus linguistics, content analysis, software engineering, and electronic dictionaries

M3 - Conference contribution/Paper

SP - 7

EP - 12

BT - Proceedings of the beyond named entity recognition semantic labelling for NLP tasks workshop, Lisbon, Portugal, 2004

CY - Lisbon

T2 - Beyond Named Entity Recognition Semantic labelling for NLP tasks

Y2 - 25 May 2004 through 25 May 2004

ER -