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
- Editor:
- Dongyan Zhao
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 142–145
- Language:
- URL:
- https://aclanthology.org/C18-2031
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/C18-2031.pdf