Andrea Horbach

Dr. Andrea Horbach Foto: Henrik Schipper

Dr. Andrea Horbach

Leiterin der Nachwuchsgruppe EduNLP

E-Mail: andrea.horbach

Universitätsstr. 27 – PRG / Gebäude 5
58097 Hagen

Was ist meine Rolle in CATALPA ?

Als Computerlinguistin leite ich die Nachwuchsgruppe EduNLP. Mich fasziniert, wie der Computer menschliche Sprache analysieren, verstehen und selbst produzieren kann - obwohl Sprache so komplex und mehrdeutig ist. Ich möchte durch automatische Sprachverarbeitung Lernende dabei unterstützen, bessere Texte zu schreiben, und Lehrenden ermöglichen, Texte effizienter auszuwerten.

Warum CATALPA ?

Für uns Menschen ist Sprache in den meisten Situationen das Kommunikationsmittel der Wahl. Gerade in der online-Lehre musste man bisher häufig auf „sprachfreie“ Aufgabenformate ausweichen, weil sie der Computer besser automatisch auswerten kann. Ich möchte in CATALPA dazu beitragen, dass digitale Lehre sich an den Anforderungen der Lernenden ausrichten kann und sich nicht der technischen Machbarkeit unterordnen muss.

    • Leiterin der Nachwuchsforschungsgruppe “Educational Natural Language Processing” bei CATALPA (vormals D²L² “Digitalisierung, Diversität und Lebenslanges Lernen. Konsequenzen für die Hochschulbildung“), FernUniversität in Hagen (seit 12/2021)
    • Wissenschaftliche Mitarbeiterin, Language Technology Lab, Universität Duisburg-Essen (10/2016 - 11/2021)
    • PhD in Computerlinguistik, Universität des Saarlandes, Saarbrücken (2018)
    • Wissenschaftliche Mitarbeiterin/Doktorandin am Institut für Computerlinguistik, Universität des Saarlandes, Saarbrücken (04/2010 – 09/2016)
    • Diplom in Computerlinguistik, Universität des Saarlandes, Saarbrücken (2008)

    • Sprachverarbeitung für Bildungsanwendungen
    • Automatische Bewertung von Freitextaufgaben
    • Aufgaben- und Feedbackgenerierung

  • Eine vollständige Liste meiner Publikationen findet sich auf Google Scholar.

    Zesch, T., Horbach, A., & Zehner, F. (2023) To Score or Not to Score: Factors Influencing Performance and Feasibility of Automatic Content Scoring of Text Responses. Educational Measurement: Issues and Practice. https://onlinelibrary.wiley.com/doi/full/10.1111/emip.12544

    Horbach, A., Laarmann-Quante, R., Liebenow, L., Jansen, T., Keller, S., Meyer, J., ... & Fleckenstein, J. (2022, December). Bringing Automatic Scoring into the Classroom–Measuring the Impact of Automated Analytic Feedback on Student Writing Performance. In Swedish Language Technology Conference and NLP4CALL (pp. 72-83). https://ecp.ep.liu.se/index.php/sltc/article/view/580

    Ding, Y., Bexte, M., & Horbach, A. (2022). Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications. https://aclanthology.org/2022.bea-1.17/

    Bexte, M., Horbach, A., & Zesch, T. (2022). Similarity-based Content Scoring - How to Make S-BERT Keep up with BERT. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications.Laarmann-Quante, R., Schwarz, L., Horbach, A., & Zesch, T. (2022). https://aclanthology.org/2022.bea-1.16/

    Meet me at the ribary’ – Acceptability of spelling variants in free-text answers to listening comprehension prompts. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications. https://aclanthology.org/2022.bea-1.22/

    Bexte, M., Laarmann-Quante, R., Horbach, A., & Zesch, T. (2022). LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC-2022). https://aclanthology.org/2022.lrec-1.73/

    Bexte, M., Horbach, A., & Zesch, T. (2021). Implicit Phenomena in Short-answer Scoring Data. In Proceedings of the First Workshop on Understanding Implicit and Underspecified Language. https://aclanthology.org/2021.unimplicit-1.2/

    Horbach, A., Aldabe, I., Bexte, M., Lopez de Lacalle, O., & Maritxalar, M. (2020). Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions. In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC-2020). https://aclanthology.org/2020.lrec-1.217/

    Ding, Y., Riordan, B., Horbach, A., Cahill, A., & Zesch, T. (2020). Don’t take “nswvtnvakgxpm” for an answer - The surprising vulnerability of automatic content scoring systems to adversarial input. In Proceedings of the 28th International Conference on Computational Linguistics(COLING 2020). https://aclanthology.org/2020.coling-main.76/

    Ding, Y., Horbach, A., Wang, H., Song, X., & Zesch, T. (2020). Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels. In Proceedings of the 1st conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing(AACL-IJCNLP 2020). https://aclanthology.org/2020.aacl-main.37/

    Horbach, A., & Zesch, T. (2019). The Influence of Variance in Learner Answers on Automatic Content Scoring. Frontiers in Education, 4, 28. https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00047459/Horbach_Zesch_Influence_Variance.pdf

    Zesch, T., Horbach, A., Goggin, M., & Wrede-Jackes, J. (2018). A flexible online system for curating reduced redundancy language exercises and tests. In P. Taalas, J. Jalkanen, L. Bradley, & S. Thouësny (Eds.), Future-proof CALL: language learning as exploration and encounters – short papers from EUROCALL 2018 (pp. 319–324). https://doi.org/10.14705/rpnet.2018.26.857

    Horbach, A., Stennmanns, S., & Zesch, T. (2018). Cross-lingual Content Scoring. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 410–419). New Orleans, LA, USA: Association for Computational Linguistics. http://www.aclweb.org/anthology/W18-0550

    Horbach, A., & Pinkal, M. (2018). Semi-Supervised Clustering for Short Answer Scoring. In LREC. Miyazaki, Japan. http://www.lrec-conf.org/proceedings/lrec2018/pdf/427.pdf

    Zesch, T., & Horbach, A. (2018). ESCRITO - An NLP-Enhanced Educational Scoring Toolkit. In Proceedings of the Language Resources and Evaluation Conference (LREC). Miyazaki, Japan: European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2018/pdf/590.pdf

    Horbach, A., Ding, Y., & Zesch, T. (2017). The Influence of Spelling Errors on Content Scoring Performance. In Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017) (pp. 45–53). Taipei, Taiwan: Asian Federation of Natural Language Processing. https://www.aclweb.org/anthology/W17-5908

    Horbach, A., Scholten-Akoun, D., Ding, Y., & Zesch, T. (2017). Fine-grained essay scoring of a complex writing task for native speakers. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 357–366). Copenhagen, Denmark: Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-5040

    Riordan, B., Horbach, A., Cahill, A., Zesch, T., & Lee, C. M. (2017). Investigating neural architectures for short answer scoring. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 159–168). Copenhagen, Denmark: Association for Computational Linguistics. https://aclanthology.org/W17-5017/

    Keiper, L., Horbach, A., & Thater, S. (2016). Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (pp. 198–205). Portorož, Slovenia: European Language Resources Association (ELRA). Retrieved from https://www.aclweb.org/anthology/L16-1030

    Horbach, A., & Palmer, A. (2016). Investigating Active Learning for Short-Answer Scoring. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 301–311). San Diego, CA: Association for Computational https://aclanthology.org/W16-0535/

    Horbach, A., Thater, S., Steffen, D., Fischer, P. M., Witt, A., & Pinkal, M. (2015). Internet corpora: A challenge for linguistic processing. Datenbank-Spektrum, 15(1), 41–47. https://link.springer.com/article/10.1007%2Fs13222-014-0172-z

    Ostermann, S., Horbach, A., & Pinkal, M. (2015). Annotating Entailment Relations for Shortanswer Questions. In Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications (pp. 49–58). Beijing, China: Association for Computational Linguistics. https://aclanthology.org/W15-4408/

    Horbach, A., Poitz, J., & Palmer, A. (2015). Using Shallow Syntactic Features to Measure Influences of L1 and Proficiency Level in EFL Writings. In Proceedings of the fourth workshop on NLP for computer-assisted language learning (pp. 21–34). Vilnius, Lithuania: LiU Electronic Press. https://www.aclweb.org/anthology/W15-1903

    Koleva, N., Horbach, A., Palmer, A., Ostermann, S., & Pinkal, M. (2014). Paraphrase Detection for Short Answer Scoring. In Proceedings of the third workshop on NLP for computer-assisted language learning (pp. 59–73). Uppsala, Sweden: LiU Electronic Press. https://www.aclweb.org/anthology/W14-3505

    Horbach, A., Palmer, A., & Wolska, M. (2014). Finding a Tradeoff between Accuracy and Rater’s Workload in Grading Clustered Short Answers. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014) (pp. 588–595). Reykjavik, Iceland: European Languages Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2014/pdf/887_Paper.pdf

    Horbach, A., Palmer, A., & Pinkal, M. (2013). Using the text to evaluate short answers for reading comprehension exercises. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity (pp. 286–295). Atlanta, Georgia, USA: Association for Computational Linguistics. https://www.aclweb.org/anthology/S13-1041

  • Wenn Sie Interesse an einer Abschlussarbeit im Bereich Educational NLP haben, sprechen Sie mich gerne an!

    Die folgende Liste von vergangenen Abschlussarbeiten, die ich betreut habe, zeigt Ihnen beispielhaft die Bandbreite möglicher Themen:

    • Evaluation of picture description tasks using visio-linguistic neural models (Marie Bexte 2021)
    • The influence of vocabulary features on the automatic evaluation of English learner essays (Viet Phe Nguyen, 2021)
    • Comparative visualization of essays (Tim Ludwig, 2020)
    • Investigating transformer-based methods for short answer scoring (Ahmed Nahzan Ilyas ​​​​​​, 2020)
    • Influence of grammatical error correction on Chinese Essay Scoring(Bingxin Chen, 2020)
    • Methods for automatically classifying errors in one-word answers to listening comprehension tasks (Frederik Wollatz, 2020)
    • Bootstrapping a conversational tutor by semi-automatically analyzing interaction data (Ankita Mandal, 2020)
    • English-Chinese cross-lingual scoring of short answer questions (Xuefeng Song, 2019)
    • Chinese Short Answer Scoring (Haoshi Wang, 2019)
    • Adversarial Examples for Evaluating Automatic Content Scoring Systems (Yuning Ding, 2019)
    • Cross-lingual content scoring (Sebastian Stennmanns, 2018)
    • Topic-sensitive methods for automatic spelling correction (Ruishen Liu, 2018)
    • Cross-task scoring of complex writing tasks using domain adaptation and task-independent features (Marie Bexte, 2018)
    • A Comparative Evaluation of German Grapheme-to-Phoneme Conversion Libraries (Rüdiger Fröhlich, 2018)
    • The influence of spelling errors on the performance of short-answer scoring systems (Yuning Ding, 2017)
    • The impact of language errors and the performance of native language identification (Yufei Mu, 2017)
    • Exploring the Role of Textual Entailment for Short Answer Scoring (Simon Ostermann, 2015)
    • Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario (Lena Keiper, 2015)
    • Applying POS-Based Language Models of Learner Data for Native Language Identification and Error Detection (Jonathan Poitz, 2014)
    • Paraphrase Fragment Extraction for German with Applications for Short Answer Scoring (Nikolina Koleva, 2014)
Forschungszentrum CATALPA | 29.03.2023