Recognition of affect, judgment, and appreciation in text

Alena Neviarouskaya, Helmut Prendinger, Mitsuru Ishizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Citations (Scopus)

Abstract

The main task we address in our research is classification of text using fine-grained attitude labels. The developed@AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine grained level (14 labels) was 62%, on the middle level (7 labels) - 71%, and on the top level (3 labels) - 88%.

Original languageEnglish
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages806-814
Number of pages9
Volume2
Publication statusPublished - 2010
Externally publishedYes
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing
Duration: 2010 Aug 232010 Aug 27

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CityBeijing
Period10/8/2310/8/27

Fingerprint

Labels
evaluation
experience
Evaluation
Compositionality
Verb Classes
Personal Experience

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Cite this

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010). Recognition of affect, judgment, and appreciation in text. In Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 806-814)

Recognition of affect, judgment, and appreciation in text. / Neviarouskaya, Alena; Prendinger, Helmut; Ishizuka, Mitsuru.

Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. Vol. 2 2010. p. 806-814.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Neviarouskaya, A, Prendinger, H & Ishizuka, M 2010, Recognition of affect, judgment, and appreciation in text. in Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. vol. 2, pp. 806-814, 23rd International Conference on Computational Linguistics, Coling 2010, Beijing, 10/8/23.
Neviarouskaya A, Prendinger H, Ishizuka M. Recognition of affect, judgment, and appreciation in text. In Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. Vol. 2. 2010. p. 806-814
Neviarouskaya, Alena ; Prendinger, Helmut ; Ishizuka, Mitsuru. / Recognition of affect, judgment, and appreciation in text. Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. Vol. 2 2010. pp. 806-814
@inproceedings{6bc9e2b3cd624f9a8bae326d7dc30e00,
title = "Recognition of affect, judgment, and appreciation in text",
abstract = "The main task we address in our research is classification of text using fine-grained attitude labels. The developed@AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine grained level (14 labels) was 62{\%}, on the middle level (7 labels) - 71{\%}, and on the top level (3 labels) - 88{\%}.",
author = "Alena Neviarouskaya and Helmut Prendinger and Mitsuru Ishizuka",
year = "2010",
language = "English",
volume = "2",
pages = "806--814",
booktitle = "Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference",

}

TY - GEN

T1 - Recognition of affect, judgment, and appreciation in text

AU - Neviarouskaya, Alena

AU - Prendinger, Helmut

AU - Ishizuka, Mitsuru

PY - 2010

Y1 - 2010

N2 - The main task we address in our research is classification of text using fine-grained attitude labels. The developed@AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine grained level (14 labels) was 62%, on the middle level (7 labels) - 71%, and on the top level (3 labels) - 88%.

AB - The main task we address in our research is classification of text using fine-grained attitude labels. The developed@AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine grained level (14 labels) was 62%, on the middle level (7 labels) - 71%, and on the top level (3 labels) - 88%.

UR - http://www.scopus.com/inward/record.url?scp=80053395625&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053395625&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80053395625

VL - 2

SP - 806

EP - 814

BT - Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference

ER -