@inproceedings{baumartz-etal-2018-ltv,
title = "{LTV}: Labeled Topic Vector",
author = "Baumartz, Daniel and
Uslu, Tolga and
Mehler, Alexander",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2031",
pages = "142--145",
abstract = "In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent natural network-based classifier for DDC, which we optimized using a wide range of linguistic features to achieve an F-score of 87.4{\%}. To show that our approach is language-independent, we evaluate nnDDC using up to 40 different languages. We derive a topic model based on nnDDC, which generates probability distributions over semantic units for any input on sense-, word- and text-level. Unlike related approaches, however, these probabilities are estimated by means of nnDDC so that each dimension of the resulting vector representation is uniquely labeled by a DDC class. In this way, we introduce a neural network-based Classifier-Induced Semantic Space (nnCISS).",
}
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<abstract>In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent natural network-based classifier for DDC, which we optimized using a wide range of linguistic features to achieve an F-score of 87.4%. To show that our approach is language-independent, we evaluate nnDDC using up to 40 different languages. We derive a topic model based on nnDDC, which generates probability distributions over semantic units for any input on sense-, word- and text-level. Unlike related approaches, however, these probabilities are estimated by means of nnDDC so that each dimension of the resulting vector representation is uniquely labeled by a DDC class. In this way, we introduce a neural network-based Classifier-Induced Semantic Space (nnCISS).</abstract>
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%0 Conference Proceedings
%T LTV: Labeled Topic Vector
%A Baumartz, Daniel
%A Uslu, Tolga
%A Mehler, Alexander
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F baumartz-etal-2018-ltv
%X In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent natural network-based classifier for DDC, which we optimized using a wide range of linguistic features to achieve an F-score of 87.4%. To show that our approach is language-independent, we evaluate nnDDC using up to 40 different languages. We derive a topic model based on nnDDC, which generates probability distributions over semantic units for any input on sense-, word- and text-level. Unlike related approaches, however, these probabilities are estimated by means of nnDDC so that each dimension of the resulting vector representation is uniquely labeled by a DDC class. In this way, we introduce a neural network-based Classifier-Induced Semantic Space (nnCISS).
%U https://aclanthology.org/C18-2031
%P 142-145
Markdown (Informal)
[LTV: Labeled Topic Vector](https://aclanthology.org/C18-2031) (Baumartz et al., COLING 2018)
ACL
- Daniel Baumartz, Tolga Uslu, and Alexander Mehler. 2018. LTV: Labeled Topic Vector. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 142–145, Santa Fe, New Mexico. Association for Computational Linguistics.