Tolga Uslu

M.Sc. Computer Science
Staff member

 

 

ContactPublications

Total: 20

2019 (2)

  • A. Mehler, T. Uslu, R. Gleim, and D. Baumartz, “text2ddc meets Literature – Ein Verfahren für die Analyse und Visualisierung thematischer Makrostrukturen,” in Proceedings of the 6th Digital Humanities Conference in the German-speaking Countries, DHd 2019, 2019. accepted
    [BibTeX]

    @InProceedings{Mehler:Uslu:Gleim:Baumartz:2019,
      Author         = {Mehler, Alexander and Uslu, Tolga and Gleim, Rüdiger and Baumartz, Daniel},
      Title          = {{text2ddc meets Literature - Ein Verfahren für die Analyse und Visualisierung thematischer Makrostrukturen}},
      BookTitle      = {Proceedings of the 6th Digital Humanities Conference in the German-speaking Countries, DHd 2019},
      Series         = {DHd 2019},
      location       = {Frankfurt, Germany},
      note      = {accepted},
      year           = 2019
    }
  • W. Hemati, A. Mehler, T. Uslu, and G. Abrami, “Der TextImager als Front- und Backend für das verteilte NLP von Big Digital Humanities Data,” in Proceedings of the 6th Digital Humanities Conference in the German-speaking Countries, DHd 2019, 2019. accepted
    [BibTeX]

    @InProceedings{Hemati:Mehler:Uslu:Abrami:2019,
      Author         = {Hemati, Wahed and Mehler, Alexander and Uslu, Tolga and Abrami, Giuseppe},
      Title          = {{Der TextImager als Front- und Backend für das verteilte NLP von Big Digital Humanities Data}},
      BookTitle      = {Proceedings of the 6th Digital Humanities Conference in the German-speaking Countries, DHd 2019},
      Series         = {DHd 2019},
      location       = {Frankfurt, Germany},
      note      = {accepted},
      year           = 2019
    }

2018 (11)

  • R. Gleim, S. Eger, A. Mehler, T. Uslu, W. Hemati, A. Lücking, A. Henlein, S. Kahlsdorf, and A. Hoenen, “Practitioner’s view: A comparison and a survey of lemmatization and morphological tagging in German and Latin,” Journal of Language Modeling, 2018. accepted
    [BibTeX]

    @article{Gleim:Eger:Mehler:2018,
      author    = {Gleim, R\"{u}diger and Eger, Steffen and Mehler, Alexander and Uslu, Tolga and Hemati, Wahed and L\"{u}cking, Andy and Henlein, Alexander and Kahlsdorf, Sven and Hoenen, Armin},
      title     = {Practitioner's view: A comparison and a survey of lemmatization and morphological tagging in German and Latin},
      journal   = {{Journal of Language Modeling}},
      year      = {2018},
      note = {accepted}
    }
  • [PDF] T. Uslu and A. Mehler, “PolyViz: a Visualization System for a Special Kind of Multipartite Graphs,” in Proceedings of the IEEE VIS 2018, 2018.
    [BibTeX]

    @InProceedings{Uslu:Mehler:2018,
    Author = {Tolga Uslu and Alexander Mehler},
    Title = {{PolyViz}: a Visualization System for a Special Kind of Multipartite Graphs},
    BookTitle = {Proceedings of the IEEE VIS 2018},
    Series = {IEEE VIS 2018},
    location = {Berlin, Germany},
    pdf = {https://www.texttechnologylab.org/wp-content/uploads/2018/07/polyviz-visualization-system.pdf},
    year = 2018
    }
  • [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.
    [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}
    }
  • [https://doi.org/10.3897/biss.2.25876] [DOI] C. Driller, M. Koch, M. Schmidt, C. Weiland, T. Hörnschemeyer, T. Hickler, G. Abrami, S. Ahmed, R. Gleim, W. Hemati, T. Uslu, A. Mehler, A. Pachzelt, J. Rexhepi, T. Risse, J. Schuster, G. Kasperek, and A. Hausinger, “Workflow and Current Achievements of BIOfid, an Information Service Mobilizing Biodiversity Data from Literature Sources,” Biodiversity Information Science and Standards, vol. 2, p. e25876, 2018.
    [Abstract] [BibTeX]

    BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.
    @article{Driller:et:al:2018,
            author = {Christine Driller and Markus Koch and Marco Schmidt and Claus Weiland and Thomas Hörnschemeyer and Thomas Hickler and Giuseppe Abrami and Sajawel Ahmed and Rüdiger Gleim and Wahed Hemati and Tolga Uslu and Alexander Mehler and Adrian Pachzelt and Jashar Rexhepi and Thomas Risse and Janina Schuster and Gerwin Kasperek and Angela Hausinger},
            title = {Workflow and Current Achievements of BIOfid, an Information Service Mobilizing Biodiversity Data from Literature Sources},
            volume = {2},
            number = {},
            year = {2018},
            doi = {10.3897/biss.2.25876},
            publisher = {Pensoft Publishers},
            abstract = {BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.},
            issn = {},
            pages = {e25876},
            URL = {https://doi.org/10.3897/biss.2.25876},
            eprint = {https://doi.org/10.3897/biss.2.25876},
            journal = {Biodiversity Information Science and Standards}
    }
  • [PDF] W. Hemati, A. Mehler, T. Uslu, D. Baumartz, and G. Abrami, “Evaluating and Integrating Databases in the Area of NLP,” in International Quantitative Linguistics Conference (QUALICO 2018), 2018.
    [Poster][BibTeX]

    @inproceedings{Hemati:Mehler:Uslu:Baumartz:Abrami:2018,
        author={Wahed Hemati and Alexander Mehler and Tolga Uslu and Daniel Baumartz and Giuseppe Abrami},
        title={Evaluating and Integrating Databases in the Area of {NLP}},
        booktitle={International Quantitative Linguistics Conference (QUALICO 2018)},
        year={2018},
        pdf={https://www.texttechnologylab.org/wp-content/uploads/2018/04/Hemat-Mehler-Uslu-Baumartz-Abrami-Qualico-2018.pdf},
        poster={https://www.texttechnologylab.org/wp-content/uploads/2018/10/qualico2018_databases_poster_hemati_mehler_uslu_baumartz_abrami.pdf},
        location={Wroclaw, Poland}
    }
  • A. Mehler, W. Hemati, T. Uslu, and A. Lücking, “A Multidimensional Model of Syntactic Dependency Trees for Authorship Attribution,” in Quantitative analysis of dependency structures, J. Jiang and H. Liu, Eds., Berlin/New York: De Gruyter, 2018.
    [Abstract] [BibTeX]

    Abstract: In this chapter we introduce a multidimensional model of syntactic dependency trees. Our ultimate goal is to generate fingerprints of such trees to predict the author of the underlying sentences. The chapter makes a first attempt to create such fingerprints for sentence categorization via the detour of text categorization. We show that at text level, aggregated dependency structures actually provide information about authorship. At the same time, we show that this does not hold for topic detection. We evaluate our model using a quarter of a million sentences collected in two corpora: the first is sampled from literary texts, the second from Wikipedia articles. As a second finding of our approach, we show that quantitative models of dependency structure do not yet allow for detecting syntactic alignment in written communication. We conclude that this is mainly due to effects of lexical alignment on syntactic alignment.
    @InCollection{Mehler:Hemati:Uslu:Luecking:2018,
      Author         = {Alexander Mehler and Wahed Hemati and Tolga Uslu and
                       Andy Lücking},
      Title          = {A Multidimensional Model of Syntactic Dependency Trees
                       for Authorship Attribution},
      BookTitle      = {Quantitative analysis of dependency structures},
      Publisher      = {De Gruyter},
      Editor         = {Jingyang Jiang and Haitao Liu},
      Address        = {Berlin/New York},
      abstract       = {Abstract: In this chapter we introduce a
    multidimensional model of syntactic dependency trees.
    Our ultimate goal is to generate fingerprints of such
    trees to predict the author of the underlying
    sentences. The chapter makes a first attempt to create
    such fingerprints for sentence categorization via the
    detour of text categorization. We show that at text
    level, aggregated dependency structures actually
    provide information about authorship. At the same time,
    we show that this does not hold for topic detection. We
    evaluate our model using a quarter of a million
    sentences collected in two corpora: the first is
    sampled from literary texts, the second from Wikipedia
    articles. As a second finding of our approach, we show
    that quantitative models of dependency structure do not
    yet allow for detecting syntactic alignment in written
    communication. We conclude that this is mainly due to
    effects of lexical alignment on syntactic alignment.},
      keywords       = {Dependency structure, Authorship attribution, Text
                       categorization, Syntactic Alignment},
      year           = 2018
    }
  • [PDF] T. Uslu, A. Mehler, and D. Meyer, “LitViz: Visualizing Literary Data by Means of text2voronoi,” in Proceedings of the Digital Humanities 2018, 2018.
    [BibTeX]

    @InProceedings{Uslu:Mehler:Meyer:2018,
      Author         = {Tolga Uslu and Alexander Mehler and Dirk Meyer},
      Title          = {{{LitViz}: Visualizing Literary Data by Means of
                       text2voronoi}},
      BookTitle      = {Proceedings of the Digital Humanities 2018},
      Series         = {DH2018},
      location       = {Mexico City, Mexico},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/LitViz.pdf},
      year           = 2018
    }
  • T. Uslu, L. Miebach, S. Wolfsgruber, M. Wagner, K. Fließbach, R. Gleim, W. Hemati, A. Henlein, and A. Mehler, “Automatic Classification in Memory Clinic Patients and in Depressive Patients,” in Proceedings of Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments (RaPID-2), 2018.
    [BibTeX]

    @InProceedings{Uslu:et:al:2018:a,
      Author         = {Tolga Uslu and Lisa Miebach and Steffen Wolfsgruber
                       and Michael Wagner and Klaus Fließbach and Rüdiger
                       Gleim and Wahed Hemati and Alexander Henlein and
                       Alexander Mehler},
      Title          = {{Automatic Classification in Memory Clinic Patients
                       and in Depressive Patients}},
      BookTitle      = {Proceedings of Resources and ProcessIng of linguistic,
                       para-linguistic and extra-linguistic Data from people
                       with various forms of cognitive/psychiatric impairments
                       (RaPID-2)},
      Series         = {RaPID},
      location       = {Miyazaki, Japan},
      year           = 2018
    }
  • [PDF] A. Mehler, R. Gleim, A. Lücking, T. Uslu, and C. Stegbauer, “On the Self-similarity of Wikipedia Talks: a Combined Discourse-analytical and Quantitative Approach,” Glottometrics, vol. 40, pp. 1-44, 2018.
    [BibTeX]

    @Article{Mehler:Gleim:Luecking:Uslu:Stegbauer:2018,
      Author         = {Alexander Mehler and Rüdiger Gleim and Andy Lücking
                       and Tolga Uslu and Christian Stegbauer},
      Title          = {On the Self-similarity of {Wikipedia} Talks: a
                       Combined Discourse-analytical and Quantitative Approach},
      Journal        = {Glottometrics},
      Volume         = {40},
      Pages          = {1-44},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/Glottometrics-Mehler.pdf},
      year           = 2018
    }
  • [PDF] T. Uslu, A. Mehler, A. Niekler, and D. Baumartz, “Towards a DDC-based Topic Network Model of Wikipedia,” in Proceedings of 2nd International Workshop on Modeling, Analysis, and Management of Social Networks and their Applications (SOCNET 2018), February 28, 2018, 2018.
    [BibTeX]

    @InProceedings{Uslu:Mehler:Niekler:Baumartz:2018,
      Author         = {Tolga Uslu and Alexander Mehler and Andreas Niekler
                       and Daniel Baumartz},
      Title          = {Towards a {DDC}-based Topic Network Model of Wikipedia},
      BookTitle      = {Proceedings of 2nd International Workshop on Modeling,
                       Analysis, and Management of Social Networks and their
                       Applications (SOCNET 2018), February 28, 2018},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/TowardsDDC.pdf},
      year           = 2018
    }
  • [PDF] T. Uslu, A. Mehler, D. Baumartz, A. Henlein, and W. Hemati, “fastSense: An Efficient Word Sense Disambiguation Classifier,” in Proceedings of the 11th edition of the Language Resources and Evaluation Conference, May 7 – 12, Miyazaki, Japan, 2018.
    [BibTeX]

    @InProceedings{Uslu:et:al:2018,
      Author         = {Tolga Uslu and Alexander Mehler and Daniel Baumartz
                       and Alexander Henlein and Wahed Hemati },
      Title          = {fastSense: An Efficient Word Sense Disambiguation
                       Classifier},
      BookTitle      = {Proceedings of the 11th edition of the Language
                       Resources and Evaluation Conference, May 7 - 12},
      Series         = {LREC 2018},
      Address        = {Miyazaki, Japan},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/fastSense.pdf},
      year           = 2018
    }

2017 (4)

  • [PDF] W. Hemati, A. Mehler, and T. Uslu, “CRFVoter: Chemical Entity Mention, Gene and Protein Related Object recognition using a conglomerate of CRF based tools,” in BioCreative V.5. Proceedings, 2017.
    [BibTeX]

    @InProceedings{Hemati:Mehler:Uslu:2017,
      Author         = {Wahed Hemati and Alexander Mehler and Tolga Uslu},
      Title          = {{CRFVoter}: Chemical Entity Mention, Gene and Protein
                       Related Object recognition using a conglomerate of CRF
                       based tools},
      BookTitle      = {BioCreative V.5. Proceedings},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/CRFVoter.pdf},
      year           = 2017
    }
  • [PDF] W. Hemati, T. Uslu, and A. Mehler, “TextImager as an interface to BeCalm,” in BioCreative V.5. Proceedings, 2017.
    [BibTeX]

    @InProceedings{Hemati:Uslu:Mehler:2017,
      Author         = {Wahed Hemati and Tolga Uslu and Alexander Mehler},
      Title          = {{TextImager} as an interface to {BeCalm}},
      BookTitle      = {BioCreative V.5. Proceedings},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/TextImager_BeCalm.pdf},
      year           = 2017
    }
  • A. Mehler, R. Gleim, W. Hemati, and T. Uslu, “Skalenfreie online soziale Lexika am Beispiel von Wiktionary,” in Proceedings of 53rd Annual Conference of the Institut für Deutsche Sprache (IDS), March 14-16, Mannheim, Germany, Berlin, 2017. In German. Title translates into: Scale-free online-social Lexika by Example of Wiktionary
    [Abstract] [BibTeX]

    In English: The paper deals with characteristics of the structural, thematic and participatory dynamics of collaboratively generated lexical networks. This is done by example of Wiktionary. Starting from a network-theoretical model in terms of so-called multi-layer networks, we describe Wiktionary as a scale-free lexicon. Systems of this sort are characterized by the fact that their content-related dynamics is determined by the underlying dynamics of collaborating authors. This happens in a way that social structure imprints on content structure. According to this conception, the unequal distribution of the activities of authors results in a correspondingly unequal distribution of the information units documented within the lexicon. The paper focuses on foundations for describing such systems starting from a parameter space which requires to deal with Wiktionary as an issue in big data analysis.  In German: Der Beitrag thematisiert Eigenschaften der strukturellen, thematischen und partizipativen Dynamik kollaborativ erzeugter lexikalischer Netzwerke am Beispiel von Wiktionary. Ausgehend von einem netzwerktheoretischen Modell in Form so genannter Mehrebenennetzwerke wird Wiktionary als ein skalenfreies Lexikon beschrieben. Systeme dieser Art zeichnen sich dadurch aus, dass ihre inhaltliche Dynamik durch die zugrundeliegende Kollaborationsdynamik bestimmt wird, und zwar so, dass sich die soziale Struktur der entsprechenden inhaltlichen Struktur aufprägt. Dieser Auffassung gemäß führt die Ungleichverteilung der Aktivitäten von Lexikonproduzenten zu einer analogen Ungleichverteilung der im Lexikon dokumentierten Informationseinheiten. Der Beitrag thematisiert Grundlagen zur Beschreibung solcher Systeme ausgehend von einem Parameterraum, welcher die netzwerkanalytische Betrachtung von Wiktionary als Big-Data-Problem darstellt.
    @InProceedings{Mehler:Gleim:Hemati:Uslu:2017,
      Author         = {Alexander Mehler and Rüdiger Gleim and Wahed Hemati
                       and Tolga Uslu},
      Title          = {{Skalenfreie online soziale Lexika am Beispiel von
                       Wiktionary}},
      BookTitle      = {Proceedings of 53rd Annual Conference of the Institut
                       für Deutsche Sprache (IDS), March 14-16, Mannheim,
                       Germany},
      Editor         = {Stefan Engelberg and Henning Lobin and Kathrin Steyer
                       and Sascha Wolfer},
      Address        = {Berlin},
      Publisher      = {De Gruyter},
      Note           = {In German. Title translates into: Scale-free
                       online-social Lexika by Example of Wiktionary},
      abstract       = {In English: The paper deals with characteristics of
    the structural, thematic and participatory dynamics of
    collaboratively generated lexical networks. This is
    done by example of Wiktionary. Starting from a
    network-theoretical model in terms of so-called
    multi-layer networks, we describe Wiktionary as a
    scale-free lexicon. Systems of this sort are
    characterized by the fact that their content-related
    dynamics is determined by the underlying dynamics of
    collaborating authors. This happens in a way that
    social structure imprints on content structure.
    According to this conception, the unequal distribution
    of the activities of authors results in a
    correspondingly unequal distribution of the information
    units documented within the lexicon. The paper focuses
    on foundations for describing such systems starting
    from a parameter space which requires to deal with
    Wiktionary as an issue in big data analysis. 
    In German:
    Der Beitrag thematisiert Eigenschaften der
    strukturellen, thematischen und partizipativen Dynamik
    kollaborativ erzeugter lexikalischer Netzwerke am
    Beispiel von Wiktionary. Ausgehend von einem
    netzwerktheoretischen Modell in Form so genannter
    Mehrebenennetzwerke wird Wiktionary als ein
    skalenfreies Lexikon beschrieben. Systeme dieser Art
    zeichnen sich dadurch aus, dass ihre inhaltliche
    Dynamik durch die zugrundeliegende
    Kollaborationsdynamik bestimmt wird, und zwar so, dass
    sich die soziale Struktur der entsprechenden
    inhaltlichen Struktur aufprägt. Dieser Auffassung
    gemäß führt die Ungleichverteilung der Aktivitäten
    von Lexikonproduzenten zu einer analogen
    Ungleichverteilung der im Lexikon dokumentierten
    Informationseinheiten. Der Beitrag thematisiert
    Grundlagen zur Beschreibung solcher Systeme ausgehend
    von einem Parameterraum, welcher die
    netzwerkanalytische Betrachtung von Wiktionary als
    Big-Data-Problem darstellt.},
      year           = 2017
    }
  • [PDF] T. Uslu, W. Hemati, A. Mehler, and D. Baumartz, “TextImager as a Generic Interface to R,” in Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), 2017.
    [BibTeX]

    @InProceedings{Uslu:Hemati:Mehler:Baumartz:2017,
      Author         = {Tolga Uslu and Wahed Hemati and Alexander Mehler and
                       Daniel Baumartz},
      Title          = {{TextImager} as a Generic Interface to {R}},
      BookTitle      = {Software Demonstrations of the 15th Conference of the
                       European Chapter of the Association for Computational
                       Linguistics (EACL 2017)},
      location       = {Valencia, Spain},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/TextImager.pdf},
      year           = 2017
    }

2016 (3)

  • [PDF] W. Hemati, T. Uslu, and A. Mehler, “TextImager: a Distributed UIMA-based System for NLP,” in Proceedings of the COLING 2016 System Demonstrations, 2016.
    [BibTeX]

    @InProceedings{Hemati:Uslu:Mehler:2016,
      Author         = {Wahed Hemati and Tolga Uslu and Alexander Mehler},
      Title          = {TextImager: a Distributed UIMA-based System for NLP},
      BookTitle      = {Proceedings of the COLING 2016 System Demonstrations},
      Organization   = {Federated Conference on Computer Science and
                       Information Systems},
      location       = {Osaka, Japan},
      pdf            = {https://www.texttechnologylab.org/wp-content/uploads/2018/03/TextImager2016.pdf},
      year           = 2016
    }
  • [PDF] A. Mehler, T. Uslu, and W. Hemati, “Text2voronoi: An Image-driven Approach to Differential Diagnosis,” in Proceedings of the 5th Workshop on Vision and Language (VL’16) hosted by the 54th Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, 2016.
    [BibTeX]

    @InProceedings{Mehler:Uslu:Hemati:2016,
      Author         = {Alexander Mehler and Tolga Uslu and Wahed Hemati},
      Title          = {Text2voronoi: An Image-driven Approach to Differential
                       Diagnosis},
      BookTitle      = {Proceedings of the 5th Workshop on Vision and Language
                       (VL'16) hosted by the 54th Annual Meeting of the
                       Association for Computational Linguistics (ACL), Berlin},
      pdf            = {https://aclweb.org/anthology/W/W16/W16-3212.pdf},
      year           = 2016
    }
  • [DOI] A. Mehler, R. Gleim, T. vor der Brück, W. Hemati, T. Uslu, and S. Eger, “Wikidition: Automatic Lexiconization and Linkification of Text Corpora,” Information Technology, pp. 70-79, 2016.
    [Abstract] [BibTeX]

    We introduce a new text technology, called Wikidition, which automatically generates large scale editions of corpora of natural language texts. Wikidition combines a wide range of text mining tools for automatically linking lexical, sentential and textual units. This includes the extraction of corpus-specific lexica down to the level of syntactic words and their grammatical categories. To this end, we introduce a novel measure of text reuse and exemplify Wikidition by means of the capitularies, that is, a corpus of Medieval Latin texts.
    @Article{Mehler:et:al:2016,
      Author         = {Alexander Mehler and Rüdiger Gleim and Tim vor der
                       Brück and Wahed Hemati and Tolga Uslu and Steffen Eger},
      Title          = {Wikidition: Automatic Lexiconization and
                       Linkification of Text Corpora},
      Journal        = {Information Technology},
      Pages          = {70-79},
      abstract       = {We introduce a new text technology, called Wikidition,
    which automatically generates large scale editions of
    corpora of natural language texts. Wikidition combines
    a wide range of text mining tools for automatically
    linking lexical, sentential and textual units. This
    includes the extraction of corpus-specific lexica down
    to the level of syntactic words and their grammatical
    categories. To this end, we introduce a novel measure
    of text reuse and exemplify Wikidition by means of the
    capitularies, that is, a corpus of Medieval Latin
    texts.},
      doi            = {10.1515/itit-2015-0035},
      year           = 2016
    }