text2ddc

text2ddc is 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 text2ddc using up to 40 different languages.
We derive a topic model based on text2ddc, 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 text2ddc 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.

Total: 1

• 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.
  [Abstract] [BibTeX]

  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).

  @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}
  }

Total: 1

• T. Uslu, A. Mehler, and D. Baumartz, “Computing Classifier-based Embeddings with the Help of text2ddc,” in Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing, (CICLing 2019), 2019.
  [BibTeX]

  @inproceedings{Uslu:Mehler:Baumartz:2019,
    author = "Uslu, Tolga and Mehler, Alexander and Baumartz, Daniel",
    booktitle = "{Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing, (CICLing 2019)}",
    location = "La Rochelle, France",
    series = "{CICLing 2019}",
    title = "{Computing Classifier-based Embeddings with the Help of text2ddc}",
    year = 2019
  }