Dr. Tolga Uslu

External habilitation candidate

ContactPublications

Total: 30

2021 (3)

A. Mehler, D. Baumartz, and T. Uslu, “SemioGraphs: Visualizing Topic Networks as Mulit-Codal Graphs,” in International Quantitative Linguistics Conference (QUALICO 2021), 2021.
[Poster][BibTeX]

@InProceedings{Mehler:Uslu:Baumartz:2021,
  Author         = {Mehler, Alexander and Baumartz, Daniel and Uslu, Tolga},
  Title          = {{SemioGraphs:} Visualizing Topic Networks as Mulit-Codal Graphs},
  BookTitle      = {International Quantitative Linguistics Conference (QUALICO 2021)},
  Series         = {QUALICO 2021},
  location       = {Tokyo, Japan},
  year           = {2021},
  poster   = {https://www.texttechnologylab.org/files/Qualico_2021_Semiograph_Poster.pdf}
}

A. Lücking, S. Brückner, G. Abrami, T. Uslu, and A. Mehler, “Computational linguistic assessment of textbooks and online texts by means of threshold concepts in economics,” Frontiers in Education, 2021.
[BibTeX]

@article{Luecking:Brueckner:Abrami:Uslu:Mehler:2021,
  journal =     {Frontiers in Education},
  doi =         {10.3389/feduc.2020.578475},
  title =     {Computational linguistic assessment of textbooks and 
                  online texts by means of threshold concepts in
                  economics},
  author =     {L{\"u}cking, Andy and Br{\"u}ckner, Sebastian and
                  Abrami, Giuseppe and Uslu, Tolga and Mehler,
                  Alexander},
  eid =         {578475},
  url =
                  {https://www.frontiersin.org/articles/10.3389/feduc.2020.578475/},
  year = {2021}
}

A. Mehler, D. Baumartz, and T. Uslu, “SemioGraphs: Visualizing Topic Networks as Mulit-Codal Graphs,” in International Quantitative Linguistics Conference (QUALICO 2021), 2021.
[BibTeX]

@InProceedings{Mehler:Uslu:Baumartz:2021,
  Author         = {Mehler, Alexander and Baumartz, Daniel and Uslu, Tolga},
  Title          = {{SemioGraphs:} Visualizing Topic Networks as Mulit-Codal Graphs},
  BookTitle      = {International Quantitative Linguistics Conference (QUALICO 2021)},
  Series         = {QUALICO 2021},
  location       = {Tokyo, Japan},
  year           = {2021}
}

2020 (5)

T. Uslu, “Multi-document analysis : semantic analysis of large text corpora beyond topic modeling,” PhD Thesis, 2020.
[BibTeX]

@phdthesis{Uslu:2020,
  author      = {Tolga Uslu},
  title       = {Multi-document analysis : semantic analysis of large text corpora beyond topic modeling},
  pages       = {204},
  year        = {2020},
  url         = {http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/56140},
  pdf        = {http://publikationen.ub.uni-frankfurt.de/files/56140/Dissertation_Tolga_Uslu.pdf}
}

A. Mehler, W. Hemati, P. Welke, M. Konca, and T. Uslu, “Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks,” Frontiers in Education, vol. 5, p. 206, 2020.
[Abstract] [BibTeX]

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.

@article{Mehler:Hemati:Welke:Konca:Uslu:2020,
  abstract = {We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.},
  author = {Mehler, Alexander and Hemati, Wahed and Welke, Pascal and Konca, Maxim and Uslu, Tolga},
  doi = {10.3389/feduc.2020.562670},
  issn = {2504-284X},
  journal = {Frontiers in Education},
  pages = {206},
  title = {Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks},
  url = {https://www.frontiersin.org/article/10.3389/feduc.2020.562670},
        pdf = {https://www.frontiersin.org/articles/10.3389/feduc.2020.562670/pdf},
  volume = {5},
  year = {2020}
}

A. Lücking, S. Brückner, G. Abrami, T. Uslu, and A. Mehler, “Computational linguistic assessment of textbook and online learning media by means of threshold concepts in business education,” CoRR, vol. abs/2008.02096, 2020.
[BibTeX]

@article{Luecking:et:al:2020,
  author    = {Andy L{\"{u}}cking and
               Sebastian Br{\"{u}}ckner and
               Giuseppe Abrami and
               Tolga Uslu and
               Alexander Mehler},
  title     = {Computational linguistic assessment of textbook and online learning
               media by means of threshold concepts in business education},
  journal   = {CoRR},
  volume    = {abs/2008.02096},
  year      = {2020},
  url       = {https://arxiv.org/abs/2008.02096},
  archivePrefix = {arXiv},
  eprint    = {2008.02096},
  timestamp = {Fri, 07 Aug 2020 15:07:21 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2008-02096.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

A. Mehler, B. Jussen, T. Geelhaar, A. Henlein, G. Abrami, D. Baumartz, T. Uslu, and W. Hemati, “The Frankfurt Latin Lexicon. From Morphological Expansion and Word Embeddings to SemioGraphs,” Studi e Saggi Linguistici, vol. 58, iss. 1, pp. 121-155, 2020.
[Abstract] [BibTeX]

In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations. We describe recent advances in the development of lemmatizers and test them against the Capitularies corpus (comprising Frankish royal edicts, mid-6th to mid-9th century), a corpus created as a reference for processing Medieval Latin. We also consider the post-correction of lemmatizations using a limited crowdsourcing process aimed at continuous review and updating of the FLL. Starting from the texts resulting from this lemmatization process, we describe the extension of the FLL by means of word embeddings, whose interactive traversing by means of SemioGraphs completes the digital enhanced hermeneutic circle. In this way, the article argues for a more comprehensive understanding of lemmatization, encompassing classical machine learning as well as intellectual post-corrections and, in particular, human computation in the form of interpretation processes based on graph representations of the underlying lexical resources.

@article{Mehler:et:al:2020b,
    author={Mehler, Alexander and Jussen, Bernhard and Geelhaar, Tim and Henlein, Alexander and Abrami, Giuseppe and Baumartz, Daniel and Uslu, Tolga and Hemati, Wahed},
    title={{The Frankfurt Latin Lexicon. From Morphological Expansion and Word Embeddings to SemioGraphs}},
    journal={Studi e Saggi Linguistici},
    doi={10.4454/ssl.v58i1.276},
    year={2020},
    volume={58},
    number={1},
    pages={121--155},
    abstract={In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations. We describe recent advances in the development of lemmatizers and test them against the Capitularies corpus (comprising Frankish royal edicts, mid-6th to mid-9th century), a corpus created as a reference for processing Medieval Latin. We also consider the post-correction of lemmatizations using a limited crowdsourcing process aimed at continuous review and updating of the FLL. Starting from the texts resulting from this lemmatization process, we describe the extension of the FLL by means of word embeddings, whose interactive traversing by means of SemioGraphs completes the digital enhanced hermeneutic circle. In this way, the article argues for a more comprehensive understanding of lemmatization, encompassing classical machine learning as well as intellectual post-corrections and, in particular, human computation in the form of interpretation processes based on graph representations of the underlying lexical resources.},
    url={https://www.studiesaggilinguistici.it/index.php/ssl/article/view/276},
    pdf={https://www.studiesaggilinguistici.it/index.php/ssl/article/download/276/219}
}

A. Mehler, R. Gleim, R. Gaitsch, T. Uslu, and W. Hemati, “From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information,” Complexity, vol. 4, pp. 1-47, 2020.
[BibTeX]

@Article{Mehler:Gleim:Gaitsch:Uslu:Hemati:2020,
    author  = {Alexander Mehler and R{\"{u}}diger Gleim and Regina Gaitsch and Tolga Uslu and Wahed Hemati},
    title   = {From Topic Networks to Distributed Cognitive Maps: {Zipfian} Topic Universes in the Area of Volunteered Geographic Information},
    journal = {Complexity},
    volume = {4},
    doi={10.1155/2020/4607025},
    pages = {1-47},
    issuetitle = {Cognitive Network Science: A New Frontier},
    year    = {2020},
}

2019 (5)

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
}

T. Uslu, A. Mehler, C. Schulz, and D. Baumartz, “BigSense: a Word Sense Disambiguator for Big Data,” in Proceedings of the Digital Humanities 2019, (DH2019), 2019.
[BibTeX]

@inproceedings{Uslu:Mehler:Schulz:Baumartz:2019,
  author = "Uslu, Tolga and Mehler, Alexander and Schulz, Clemens and Baumartz, Daniel",
  booktitle = "{Proceedings of the Digital Humanities 2019, (DH2019)}",
  location = "Utrecht, Netherlands",
  series = "{DH2019}",
  title = "{{BigSense}: a Word Sense Disambiguator for Big Data}",
  year = 2019,
  url={https://dev.clariah.nl/files/dh2019/boa/0199.html}
}

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.
[Poster][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},
  poster   = {https://www.texttechnologylab.org/wp-content/uploads/2019/04/DHD_Poster___text2ddc_meets_Literature_Poster.pdf},
  Series         = {DHd 2019},
  pdf     = {https://www.texttechnologylab.org/wp-content/uploads/2019/04/Preprint_DHd2019_text2ddc_meets_Literature.pdf},
  location       = {Frankfurt, Germany},
  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.
[Poster][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},
  pdf      = {https://www.texttechnologylab.org/wp-content/uploads/2019/04/Der-TextImager-als-Fron-und-Backend.pdf},
  poster   = {https://www.texttechnologylab.org/wp-content/uploads/2019/04/DHD19_TextImager.pdf},
  location       = {Frankfurt, Germany},
  year           = 2019
}

R. Gleim, S. Eger, A. Mehler, T. Uslu, W. Hemati, A. Lücking, A. Henlein, S. Kahlsdorf, and A. Hoenen, “A practitioner’s view: a survey and comparison of lemmatization and morphological tagging in German and Latin,” Journal of Language Modeling, 2019.
[BibTeX]

@article{Gleim:Eger:Mehler:2019,
  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     = {A practitioner's view: a survey and comparison of lemmatization and morphological tagging in German and Latin},
  journal   = {Journal of Language Modeling},
  year      = {2019},
  pdf = {https://www.texttechnologylab.org/wp-content/uploads/2019/07/jlm-tagging.pdf},
  doi = {10.15398/jlm.v7i1.205},
  url = {http://jlm.ipipan.waw.pl/index.php/JLM/article/view/205} 
}

2018 (10)

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
}

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}
}

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}
}

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
}

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
}

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
}

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
}

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)

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
}

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
}

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)

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
}

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
}

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, vol. 58, 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},
  Volume   = {58}, 
  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
}