New COLING 2018 publication

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The following publication has been accepted at the 27th International Conference on Computational Linguistics: System demonstrations (COLING):

  • [1] LTV: Labeled Topic Vector

    • [1] [pdf] 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 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 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).},
          pdf = {https://www.texttechnologylab.org/wp-content/uploads/2018/06/coling2018.pdf}
      }