LTV: Labeled Topic Vector

Daniel Baumartz, Tolga Uslu, Alexander Mehler


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).
Anthology ID:
C18-2031
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
142–145
Language:
URL:
https://aclanthology.org/C18-2031
DOI:
Bibkey:
Cite (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.
Cite (Informal):
LTV: Labeled Topic Vector (Baumartz et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-2031.pdf