Publications

Group highlights

(For a full list see below or go to Google Scholar)

GRhOOT: Ontology of Rhetorical Figures in German

GRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language. The overall goal of building an ontology of rhetorical figures in German is not only the formal representation of different rhetorical figures, but also allowing for their easier detection, thus improving sentiment analysis, argument mining, detection of hate speech and fake news, machine translation, and many other tasks in which recognition of non-literal language plays an important role.

Ramona Kühn, Jelena Mitrović and Michael Granitzer

GRhOOT, the German RhetOrical OnTology

Network Analysis of German COVID-19 Related Discussions on Telegram

We present an effective way to create a dataset from relevant channels and groups of the messenger service Telegram, to detect clus- ters in this network, and to find influential actors. Our focus lies on the network of German COVID-19 sceptics that formed on Telegram along with growing restrictions meant to prevent the spreading of COVID-19.

Valentin Peter, Ramona Kühn, Jelena Mitrović, Michael Granitzer and Hannah Schmid-Petri

Network Analysis of German COVID-19 Related Discussions on Telegram

LOD-connected offensive language ontology and tagset enrichment

The main focus of the paper is the definitional revision and enrichment of offensive language typology, making reference to publicly available offensive language datasets and testing them on available pretrained lexical embedding systems. We review over 60 available corpora and compare tagging schemas applied there while making an attempt to explain semantic differences between particular concepts of the category OFFENSIVE in English.

Barbara Lewandowska-Tomaszczyk, Slavko Žitnik, Anna Bączkowska, Chaya Liebeskind, Jelena Mitrović and Giedre Valunaite Oleskeviciene

A finite set of classes that cover aspects of offensive language representation

Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.

Daria Kim, Maximilian Alber, Man Wai Kwok, Jelena Mitrović, Cristian Ramirez-Atencia, JesÚs Alberto RodrÍguez Pérez, Heiner Zille

The paper examines several widespread assumptions about artificial intelligence

Hatebert: Retraining bert for abusive language detection in english

We introduce HateBERT, a re-trained BERT model for abusive language detection inEnglish. The model was trained on RAL-E, a large-scale dataset of Reddit comments in Englishfrom communities banned for being offensive, abusive, or hateful that we have collected andmade available to the public. Results and trained models are published on an OSF repository.

Caselli Tommaso, Basile Valerio, Jelena Mitrović and Michael Granitzer

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH) ACL-IJCNPL 2021 2021

I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language

In this contribution, we investigate a recent dataset for offensive language in English, namely OLID/OffensEval (Zampieri et al. 2019a; Zampieri et al. 2019b), in the light of two factors proposed by Waseem et al. 2017.

Tommaso Caselli, Valerio Basile, Jelena Mitrovic, Inga Kartoziya, Michael Granitzer

12th Conference on Language Resources and Evaluation (2020)

Modeling Legal Terminology in SUMO

We discuss ontological modeling of legal terminology in formal ontologies, such is SUMO (Pease, 2001) and the possibility of utilizing its close connection to the lexical-semantic network WordNet (Fellbaum, 1998) in the legal domain. Formal systems that allow for automated semantic interpretation of law supported by lexical resources can bring forth solutions to legal reasoning tasks.

Jelena Mitrović, Adam Pease, Michael Granitzer

TOTh 2019, Terminology and Ontology; Theories and applications

Ontological representations of rhetorical figures for argument mining

This paper surveys ontological modeling of rhetorical concepts, developed for use in argument mining and other applications of computational rhetoric, projecting their future directions. We include ontological models of argument schemes applying Rhetorical Structure Theory (RST); the RhetFig proposal for modeling; the related RetFig Ontology of Rhetorical Figures for Serbian (developed by two of the authors); and the Lassoing Rhetoric project (developed by another of the authors).

Jelena Mitrović, Cliff O’Reilly, Miljana Mladenović and Siegfried Handschuh

Argument & Computation, vol. 8, no. 3, pp. 267-287, 2017

Analysis of a German Legal Citation Network

The paper introduces the creation and analysis of a German legal citation network. The network consists of over 200.000 German court cases from all levels of appeal and jurisdiction and more than 50.000 laws. References to court decisions and laws are extracted from within the decision text of the court cases and added as links to the network. We apply network-based analysis techniques to support common legal information retrieval tasks such as identification of important court decisions and laws and case similarity searches. Furthermore, we demonstrate that the German case citation network displays scale-free behaviour, similar to that of the U.S. and Austrian Supreme Courts as shown by previous research.

Tobias Milz, Michael Granitzer, Jelena Mitrović

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, 147-154, 2021

Automated identification of bias inducing words in news article susing linguistic and context-oriented features

We present a prototypical yet robust anddiverse data set for media bias research. It consists of 1,700 statements representing variousmedia bias instances and contains labels for media bias identification on the word and sentencelevel. In contrast to existing research, our data incorporate background information on theparticipants’ demographics, political ideology, and their opinion about media in general.

Timo Spinde, Lada Rudnitckaia, Jelena Mitrović, Felix Hamborg, Michael Granitzer, Bela Gipp, Karsten Donnay

Information Processing & Management 2021

 

Full List

  1. Analysis of a German Legal Citation Network
    Milz., Tobias, Granitzer., Michael, and Mitrović., Jelena
    2021
  2. A Stacking Approach for Cross-Domain Argument Identification
    Alaa, Alhamzeh, Mohamed, Bouhaouel, Előd, Egyed-Zsigmond, Jelena, Mitrović, Lionel, Brunie, and Harald, Kosch
    The 32nd International Conference on Database and Expert Systems Applications DEXA 2021 2021
  3. DistilBERT-based Argumentation Retrieval for Answering Comparative Questions
    Alaa, Alhamzeh, Mohamed, Bouhaouel, Elöd, Egyed-Zsigmond, and Jelena, Mitrović
    Conference and Labs of the Evaluation Forum CLEF 2021 2021
  4. Hatebert: Retraining bert for abusive language detection in english
    Caselli, Tommaso, Basile, Valerio, Mitrović, Jelena, and Granitzer, Michael
    Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH) ACL-IJCNPL 2021 2021
  5. Automated identification of bias inducing words in news articles using linguistic and context-oriented features
    Spinde, Timo, Rudnitckaia, Lada, Mitrović, Jelena, Hamborg, Felix, Granitzer, Michael, Gipp, Bela, and Donnay, Karsten
    Information Processing & Management 2021
  6. Design and Implementation of German Legal Decision Corpora
    Urchs, Stefanie, Mitrovic, Jelena, and Granitzer, Michael
    In Proceedings of the 13th International Conference on Agents and Artificial Intelligence 2021
  7. Language Proficiency Scoring
    Arhiliuc, Cristina, Mitrović, Jelena, and Granitzer, Michael
    In Proceedings of the 12th Language Resources and Evaluation Conference 2020
  8. Cognitive Modeling in Computational Rhetoric: Litotes, Containment and the Unexcluded Middle.
    Mitrovic, Jelena, O’Reilly, Cliff, Harris, Randy Allen, and Granitzer, Michael
    In ICAART (2) 2020
  9. Grupato at semeval-2020 task 12: Retraining mbert on social media and fine-tuned offensive language models
    Colla, Davide, Caselli, Tommaso, Basile, Valerio, Mitrović, Jelena, and Granitzer, Michael
    In Proceedings of the Fourteenth Workshop on Semantic Evaluation 2020
  10. Heterogeneous photocatalytic degradation of anthraquinone dye Reactive Blue 19: optimization, comparison between processes and identification of intermediate products
    Vučić, Miljana D Radović, Mitrović, Jelena Z, Kostić, Miloš M, Velinov, Nena D, Najdanović, Slobodan M, Bojić, Danijela V, and Bojić, Aleksandar Lj
    Water SA 2020
  11. NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish
    Hussein, Omar, Sfar, Hachem, Mitrović, Jelena, and Granitzer, Michael
    In Proceedings of the Fourteenth Workshop on Semantic Evaluation 2020
  12. Towards Classifying Parts of German Legal Writing Styles in German Legal Judgments
    Urchs, Stefanie, Mitrović, Jelena, and Granitzer, Michael
    In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT) 2020
  13. Multi-word Expressions for Abusive Speech Detection in Serbian
    Stankovic, Ranka, Mitrović, Jelena, Jokic, Danka, and Krstev, Cvetana
    In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons 2020
  14. Language Proficiency Scoring
    Arhiliuc, Cristina, Mitrović, Jelena, and Granitzer, Michael
    In Proceedings of The 12th Language Resources and Evaluation Conference 2020
  15. nlpUP at SemEval-2020 Task 12: A Blazing Fast System for Offensive Language Detection
    Hamdy, Ehab, Mitrović, Jelena, and Granitzer, Michael
    In Proceedings of the Fourteenth Workshop on Semantic Evaluation 2020
  16. I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language
    Caselli, Tommaso, Basile, Valerio, Mitrović, Jelena, Kartoziya, Inga, and Granitzer, Michael
    In Proceedings of The 12th Language Resources and Evaluation Conference 2020
  17. Modeling Legal Terminology in SUMO
    Mitrović, Jelena, Pease, Adam, and Granitzer, Michael
    Proceedings of TOTh 2019
  18. nlpUP at SemEval-2019 task 6: A deep neural language model for offensive language detection
    Mitrović, Jelena, Birkeneder, Bastian, and Granitzer, Michael
    In Proceedings of the 13th International Workshop on Semantic Evaluation 2019
  19. upInf-Offensive Language Detection in German Tweets
    Birkeneder, Bastian, Mitrovic, Jelena, Niemeier, Julia, Teubert, Leon, and Handschuh, Siegfried
    In Proceedings of the GermEval 2018 Workshop 2018
  20. Using lexical resources for irony and sarcasm classification
    Mladenović, Miljana, Krstev, Cvetana, Mitrović, Jelena, and Stanković, Ranka
    In Proceedings of the 8th Balkan Conference in Informatics 2017
  21. Ontological representations of rhetorical figures for argument mining
    Mitrović, Jelena, O’Reilly, Cliff, Mladenović, Miljana, and Handschuh, Siegfried
    Argument & Computation 2017
  22. Hybrid sentiment analysis framework for a morphologically rich language
    Mladenović, Miljana, Mitrović, Jelena, Krstev, Cvetana, and Vitas, Duško
    Journal of Intelligent Information Systems 2016