Daniel Baumartz


2018

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FastSense: An Efficient Word Sense Disambiguation Classifier
Tolga Uslu | Alexander Mehler | Daniel Baumartz | Wahed Hemati
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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LTV: Labeled Topic Vector
Daniel Baumartz | Tolga Uslu | Alexander Mehler
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

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).

2017

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TextImager as a Generic Interface to R
Tolga Uslu | Wahed Hemati | Alexander Mehler | Daniel Baumartz
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

R is a very powerful framework for statistical modeling. Thus, it is of high importance to integrate R with state-of-the-art tools in NLP. In this paper, we present the functionality and architecture of such an integration by means of TextImager. We use the OpenCPU API to integrate R based on our own R-Server. This allows for communicating with R-packages and combining them with TextImager’s NLP-components.