Dr. Wahed Hemati

Dr. Wahed Hemati
M.Sc. Computer Science

External habilitation candidate

ContactPublications

Total: 30

2021 (1)

M. Konca, A. Mehler, D. Baumartz, and W. Hemati, “From distinguishability to informativity. A quantitative text model for detecting random texts.,” Language and Text: Data, models, information and applications, vol. 356, pp. 145-162, 2021.
[BibTeX]

@article{Konca:et:al:2021,
  title={From distinguishability to informativity. A quantitative text model for detecting random texts.},
  author={Konca, Maxim and Mehler, Alexander and Baumartz, Daniel and Hemati, Wahed},
  journal={Language and Text: Data, models, information and applications},
  volume={356},
  pages={145--162},
  year={2021},
  editor={Adam Paw{\l}owski, Jan Ma{\v{c}}utek, Sheila Embleton and George Mikros},
  publisher={John Benjamins Publishing Company},
  doi={10.1075/cilt.356.10kon}
}

2020 (7)

W. Hemati, “TextImager-VSD : large scale verb sense disambiguation and named entity recognition in the context of TextImager,” PhD Thesis, 2020.
[BibTeX]

@phdthesis{Hemati:2020,
  author      = {Wahed Hemati},
  title       = {TextImager-VSD : large scale verb sense disambiguation and named entity recognition in the context of TextImager},
  pages       = {174},
  year        = {2020},
  url        = {http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/56089},
  pdf        = {http://publikationen.ub.uni-frankfurt.de/files/56089/dissertation_Wahed_Hemati.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}
}

C. Driller, M. Koch, G. Abrami, W. Hemati, A. Lücking, A. Mehler, A. Pachzelt, and G. Kasperek, “Fast and Easy Access to Central European Biodiversity Data with BIOfid,” Biodiversity Information Science and Standards, vol. 4, p. e59157, 2020.
[Abstract] [BibTeX]

The storage of data in public repositories such as the Global Biodiversity Information Facility (GBIF) or the National Center for Biotechnology Information (NCBI) is nowadays stipulated in the policies of many publishers in order to facilitate data replication or proliferation. Species occurrence records contained in legacy printed literature are no exception to this. The extent of their digital and machine-readable availability, however, is still far from matching the existing data volume (Thessen and Parr 2014). But precisely these data are becoming more and more relevant to the investigation of ongoing loss of biodiversity. In order to extract species occurrence records at a larger scale from available publications, one has to apply specialised text mining tools. However, such tools are in short supply especially for scientific literature in the German language.The Specialised Information Service Biodiversity Research*1 BIOfid (Koch et al. 2017) aims at reducing this desideratum, inter alia, by preparing a searchable text corpus semantically enriched by a new kind of multi-label annotation. For this purpose, we feed manual annotations into automatic, machine-learning annotators. This mixture of automatic and manual methods is needed, because BIOfid approaches a new application area with respect to language (mainly German of the 19th century), text type (biological reports), and linguistic focus (technical and everyday language).We will present current results of the performance of BIOfid’s semantic search engine and the application of independent natural language processing (NLP) tools. Most of these are freely available online, such as TextImager  (Hemati et al. 2016). We will show how TextImager is tied into the BIOfid pipeline and how it is made scalable (e.g. extendible by further modules) and usable on different systems (docker containers).Further, we will provide a short introduction to generating machine-learning training data using  TextAnnotator  (Abrami et al. 2019) for multi-label annotation. Annotation reproducibility can be assessed by the implementation of inter-annotator agreement methods (Abrami et al. 2020). Beyond taxon recognition and entity linking, we place particular emphasis on location and time information. For this purpose, our annotation tag-set combines general categories and biology-specific categories (including taxonomic names) with location and time ontologies. The application of the annotation categories is regimented by annotation guidelines (Lücking et al. 2020). Within the next years, our work deliverable will be a semantically accessible and data-extractable text corpus of around two million pages. In this way, BIOfid is creating a new valuable resource that expands our knowledge of biodiversity and its determinants.

@article{Driller:et:al:2020,
  author = {Christine Driller and Markus Koch and Giuseppe Abrami and Wahed Hemati and Andy Lücking and Alexander Mehler and Adrian Pachzelt and Gerwin Kasperek},
  title = {Fast and Easy Access to Central European Biodiversity Data with BIOfid},
  volume = {4},
  number = {},
  year = {2020},
  doi = {10.3897/biss.4.59157},
  publisher = {Pensoft Publishers},
  abstract = {The storage of data in public repositories such as the Global Biodiversity Information Facility (GBIF) or the National Center for Biotechnology Information (NCBI) is nowadays stipulated in the policies of many publishers in order to facilitate data replication or proliferation. Species occurrence records contained in legacy printed literature are no exception to this. The extent of their digital and machine-readable availability, however, is still far from matching the existing data volume (Thessen and Parr 2014). But precisely these data are becoming more and more relevant to the investigation of ongoing loss of biodiversity. In order to extract species occurrence records at a larger scale from available publications, one has to apply specialised text mining tools. However, such tools are in short supply especially for scientific literature in the German language.The Specialised Information Service Biodiversity Research*1 BIOfid (Koch et al. 2017) aims at reducing this desideratum, inter alia, by preparing a searchable text corpus semantically enriched by a new kind of multi-label annotation. For this purpose, we feed manual annotations into automatic, machine-learning annotators. This mixture of automatic and manual methods is needed, because BIOfid approaches a new application area with respect to language (mainly German of the 19th century), text type (biological reports), and linguistic focus (technical and everyday language).We will present current results of the performance of BIOfid’s semantic search engine and the application of independent natural language processing (NLP) tools. Most of these are freely available online, such as TextImager  (Hemati et al. 2016). We will show how TextImager is tied into the BIOfid pipeline and how it is made scalable (e.g. extendible by further modules) and usable on different systems (docker containers).Further, we will provide a short introduction to generating machine-learning training data using  TextAnnotator  (Abrami et al. 2019) for multi-label annotation. Annotation reproducibility can be assessed by the implementation of inter-annotator agreement methods (Abrami et al. 2020). Beyond taxon recognition and entity linking, we place particular emphasis on location and time information. For this purpose, our annotation tag-set combines general categories and biology-specific categories (including taxonomic names) with location and time ontologies. The application of the annotation categories is regimented by annotation guidelines (Lücking et al. 2020). Within the next years, our work deliverable will be a semantically accessible and data-extractable text corpus of around two million pages. In this way, BIOfid is creating a new valuable resource that expands our knowledge of biodiversity and its determinants.},
  issn = {},
  pages = {e59157},
  URL = {https://doi.org/10.3897/biss.4.59157},
  eprint = {https://doi.org/10.3897/biss.4.59157},
  journal = {Biodiversity Information Science and Standards}
}

M. Stoeckel, A. Henlein, W. Hemati, and A. Mehler, “Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin,” in Proceedings of LT4HALA 2020 – 1st Workshop on Language Technologies for Historical and Ancient Languages, Marseille, France, 2020, pp. 130-135.
[Abstract] [BibTeX]

Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64\% and 87.00\%) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91\% on classical, 90.87\% on cross-genre and 87.35\% on cross-time).

@InProceedings{Stoeckel:et:al:2020,
  author    = {Stoeckel, Manuel and Henlein, Alexander and Hemati, Wahed and Mehler, Alexander},
  title     = {{Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin}},
  booktitle      = {Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association (ELRA)},
  pages     = {130--135},
  abstract  = {Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64\% and 87.00\%) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91\% on classical, 90.87\% on cross-genre and 87.35\% on cross-time).},
  url       = {https://www.aclweb.org/anthology/2020.lt4hala-1.21},
  pdf       = {http://www.lrec-conf.org/proceedings/lrec2020/workshops/LT4HALA/pdf/2020.lt4hala-1.21.pdf}

}

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}
}

J. Hildebrand, W. Hemati, and A. Mehler, “Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars,” in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille, France, 2020, pp. 5282-5290.
[Abstract] [BibTeX]

Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related “logical document structures”. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.

@InProceedings{Hildebrand:Hemati:Mehler:2020,
  Author         = {Hildebrand, Jonathan and Hemati, Wahed and Mehler, Alexander},
  Title          = {Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars},
 booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {5282--5290},
  abstract  = {Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related “logical document structures”. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.650},
  pdf      = {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.650.pdf}
}

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 (6)

M. Stoeckel, W. Hemati, and A. Mehler, “When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish,” in Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, Hong Kong, China, 2019, pp. 11-15.
[Abstract] [BibTeX]

The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76\% F1-score using pre-trained embeddings and are able to improve these results to 90.52\% F1-score using specialized embeddings.

@inproceedings{Stoeckel:Hemati:Mehler:2019,
    title = "When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in {S}panish",
    author = "Stoeckel, Manuel and Hemati, Wahed and Mehler, Alexander",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5702",
    doi = "10.18653/v1/D19-5702",
    pages = "11--15",
    abstract = "The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76{\%} F1-score using pre-trained embeddings and are able to improve these results to 90.52{\%} F1-score using specialized embeddings.",
}

A. Hunziker, H. Mammadov, W. Hemati, and A. Mehler, “Corpus2Wiki: A MediaWiki-based Tool for Automatically Generating Wikiditions in Digital Humanities,” in INF-DH-2019, Bonn, 2019.
[BibTeX]

@inproceedings{Hunziker:et:al:2019,
    author = {Hunziker, Alex and Mammadov, Hasanagha and Hemati, Wahed and Mehler, Alexander},
    title = {{Corpus2Wiki}: A MediaWiki-based Tool for Automatically Generating Wikiditions in Digital Humanities},
    booktitle = {INF-DH-2019},
    year = {2019},
    editor = {Burghardt, Manuel AND Müller-Birn, Claudia},
    publisher = {Gesellschaft für Informatik e.V.},
    address = {Bonn}
}

W. Hemati and A. Mehler, “CRFVoter: gene and protein related object recognition using a conglomerate of CRF-based tools,” Journal of Cheminformatics, vol. 11, iss. 1, p. 11, 2019.
[Abstract] [BibTeX]

Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of gene and protein related objects. For this purpose, we transform the task as posed by BioCreative V.5 into a sequence labeling problem. We present a series of sequence labeling systems that we used and adapted in our experiments for solving this task. Our experiments show how to optimize the hyperparameters of the classifiers involved. To this end, we utilize various algorithms for hyperparameter optimization. Finally, we present CRFVoter, a two-stage application of Conditional Random Field (CRF) that integrates the optimized sequence labelers from our study into one ensemble classifier.

@article{Hemati:Mehler:2019b,
  author="Hemati, Wahed and Mehler, Alexander",
  title="{{CRFVoter}: gene and protein related object recognition using a conglomerate of CRF-based tools}",
  journal={Journal of Cheminformatics},
  year="2019",
  month="Mar",
  day="14",
  volume="11",
  number="1",
  pages="11",
  abstract="Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of gene and protein related objects. For this purpose, we transform the task as posed by BioCreative V.5 into a sequence labeling problem. We present a series of sequence labeling systems that we used and adapted in our experiments for solving this task. Our experiments show how to optimize the hyperparameters of the classifiers involved. To this end, we utilize various algorithms for hyperparameter optimization. Finally, we present CRFVoter, a two-stage application of Conditional Random Field (CRF) that integrates the optimized sequence labelers from our study into one ensemble classifier.",
  issn="1758-2946",
  doi="10.1186/s13321-019-0343-x",
  url="https://doi.org/10.1186/s13321-019-0343-x"
}

W. Hemati and A. Mehler, “LSTMVoter: chemical named entity recognition using a conglomerate of sequence labeling tools,” Journal of Cheminformatics, vol. 11, iss. 1, p. 7, 2019.
[Abstract] [BibTeX]

Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier.

@article{Hemati:Mehler:2019a,
  abstract = "Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier.",
  author = "Hemati, Wahed and Mehler, Alexander",
  day = "10",
  doi = "10.1186/s13321-018-0327-2",
  issn = "1758-2946",
  journal = "Journal of Cheminformatics",
  month = "Jan",
  number = "1",
  pages = "7",
  title = "{{LSTMVoter}: chemical named entity recognition using a conglomerate of sequence labeling tools}",
  url = "https://doi.org/10.1186/s13321-018-0327-2",
  volume = "11",
  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 (9)

E. Rutherford, W. Hemati, and A. Mehler, “Corpus2Wiki: A MediaWiki based Annotation & Visualisation Tool for the Digital Humanities,” in INF-DH-2018, Bonn, 2018.
[BibTeX]

@inproceedings{Rutherford:et:al:2018,
    author = {Rutherford, Eleanor AND Hemati, Wahed AND Mehler, Alexander},
    title = {{Corpus2Wiki}: A MediaWiki based Annotation \& Visualisation Tool for the Digital Humanities},
    booktitle = {INF-DH-2018},
    year = {2018},
    editor = {Burghardt, Manuel AND Müller-Birn, Claudia},
    publisher = {Gesellschaft für Informatik e.V.},
    address = {Bonn}
}

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, R. Gleim, and D. Baumartz, “VienNA: Auf dem Weg zu einer Infrastruktur für die verteilte interaktive evolutionäre Verarbeitung natürlicher Sprache,” in Forschungsinfrastrukturen und digitale Informationssysteme in der germanistischen Sprachwissenschaft , H. Lobin, R. Schneider, and A. Witt, Eds., Berlin: De Gruyter, 2018, vol. 6.
[BibTeX]

@InCollection{Mehler:Hemati:Gleim:Baumartz:2018,
  Author         = {Alexander Mehler and Wahed Hemati and Rüdiger Gleim
                   and Daniel Baumartz},
  Title          = {{VienNA: }{Auf dem Weg zu einer Infrastruktur für die verteilte
                   interaktive evolutionäre Verarbeitung natürlicher
                   Sprache}},
  BookTitle      = {Forschungsinfrastrukturen und digitale
                   Informationssysteme in der germanistischen
                   Sprachwissenschaft },
  Publisher      = {De Gruyter},
  Editor         = {Henning Lobin and Roman Schneider and Andreas Witt},
  Volume         = {6},
  Address        = {Berlin},
  year           = 2018
}

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, 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
}

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
}

A. Mehler, O. Zlatkin-Troitschanskaia, W. Hemati, D. Molerov, A. Lücking, and S. Schmidt, “Integrating Computational Linguistic Analysis of Multilingual Learning Data and Educational Measurement Approaches to Explore Learning in Higher Education,” in Positive Learning in the Age of Information: A Blessing or a Curse?, O. Zlatkin-Troitschanskaia, G. Wittum, and A. Dengel, Eds., Wiesbaden: Springer Fachmedien Wiesbaden, 2018, pp. 145-193.
[Abstract] [BibTeX]

This chapter develops a computational linguistic model for analyzing and comparing multilingual data as well as its application to a large body of standardized assessment data from higher education. The approach employs both an automatic and a manual annotation of the data on several linguistic layers (including parts of speech, text structure and content). Quantitative features of the textual data are explored that are related to both the students' (domain-specific knowledge) test results and their level of academic experience. The respective analysis involves statistics of distance correlation, text categorization with respect to text types (questions and response options) as well as languages (English and German), and network analysis to assess dependencies between features. The correlation between correct test results of students and linguistic features of the verbal presentations of tests indicate to what extent language influences higher education test performance. It has also been found that this influence relates to specialized language. Thus, this integrative modeling approach contributes a test basis for a large-scale analysis of learning data and points to a number of subsequent, more detailed research questions.

@inbook{Mehler:et:al:2018,
  abstract = "This chapter develops a computational linguistic model for analyzing and comparing multilingual data as well as its application to a large body of standardized assessment data from higher education. The approach employs both an automatic and a manual annotation of the data on several linguistic layers (including parts of speech, text structure and content). Quantitative features of the textual data are explored that are related to both the students' (domain-specific knowledge) test results and their level of academic experience. The respective analysis involves statistics of distance correlation, text categorization with respect to text types (questions and response options) as well as languages (English and German), and network analysis to assess dependencies between features. The correlation between correct test results of students and linguistic features of the verbal presentations of tests indicate to what extent language influences higher education test performance. It has also been found that this influence relates to specialized language. Thus, this integrative modeling approach contributes a test basis for a large-scale analysis of learning data and points to a number of subsequent, more detailed research questions.",
  address = "Wiesbaden",
  author = "Mehler, Alexander and Zlatkin-Troitschanskaia, Olga and Hemati, Wahed and Molerov, Dimitri and L{\"u}cking, Andy and Schmidt, Susanne",
  booktitle = "Positive Learning in the Age of Information: A Blessing or a Curse?",
  doi = "10.1007/978-3-658-19567-0_10",
  editor = "Zlatkin-Troitschanskaia, Olga and Wittum, Gabriel and Dengel, Andreas",
  isbn = "978-3-658-19567-0",
  pages = "145--193",
  publisher = "Springer Fachmedien Wiesbaden",
  title = "Integrating Computational Linguistic Analysis of Multilingual Learning Data and Educational Measurement Approaches to Explore Learning in Higher Education",
  url = "https://doi.org/10.1007/978-3-658-19567-0_10",
  year = "2018"
}

G. Abrami, S. Ahmed, R. Gleim, W. Hemati, A. Mehler, and U. Tolga, Natural Language Processing and Text Mining for BIOfid, 2018.
[BibTeX]

@misc{Abrami:et:al:2018b,
 author = {Abrami, Giuseppe and Ahmed, Sajawel and Gleim, R{\"u}diger and Hemati, Wahed and Mehler, Alexander and Uslu Tolga},
 title = {{Natural Language Processing and Text Mining for BIOfid}},
 howpublished = {Presentation at the 1st Meeting of the Scientific Advisory Board of the BIOfid Project},
 adress = {Goethe-University, Frankfurt am Main, Germany},
 year = {2018},
 month = {March},
 day = {08},
 pdf = {}
}

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
}