## New publication!

A new publication involving CEDIFOR members has been published:

• D. Baumartz, T. Uslu, and A. Mehler, “LTV: labeled topic vector,” in Proceedings of COLING 2018, the 27th international conference on computational linguistics: system demonstrations, august 20-26, Santa Fe, New Mexico, USA, 2018.
[Bibtex]
@InProceedings{Baumartz:Uslu:Mehler:2018,
author= {Daniel Baumartz and Tolga Uslu and Alexander Mehler},
title= {{LTV}: Labeled Topic Vector},
booktitle= {Proceedings of {COLING 2018}, the 27th International Conference on Computational Linguistics: System Demonstrations, August 20-26},
year= {2018},
address= {Santa Fe, New Mexico, USA},
publisher= {The COLING 2018 Organizing Committee},
abstract= {In this paper, we present LTV, a website and an API that generate labeled topic classifications based on the \textit{Dewey Decimal Classification} (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent neural network-based classifier for DDC-related topic classification, which we optimized using a wide range of linguistic features to achieve an F-score of \numprint{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 \textit{Classifier-Induced Semantic Space} (nnCISS).},
note= {accepted},
}