About Me

Lukas Gienapp

I am a Researcher at the TEMIR group at Leipzig University. I am passionate about all things in Text Mining, Data Science, and Information Retrieval. I mainly work on deep learning and generative approaches for search.

Professional Experience

since 2022
In-Kind Member
Deep Learning, Generative Models
ScaDS.AI
since 2019
Researcher
Crowdsourcing & Evaluation, Web Search, Plagiarism Detection
TEMIR Group, Leipzig University
2017-2019
Student Assistant
Research Infrastructure, Technical Support, Experiment Assistance
Institute for Sociology, Leipzig University
2017-2019
Student Assistant
Programming, Typesetting, Research Assistance
Institute for Translatology, Leipzig University

Teaching Experience

I have given seminars and lab sessions on both bachelors and masters level covering topics in ML, NLP, and IR:

Education

2019-2022
M.Sc. Data Science
Leipzig University
2019-2022
M.Sc. Digital Humanities
Leipzig University
2016-2019
B.Sc. Digital Humanities
Leipzig University
2015-2016
B.A. Linguistics
Leipzig University
until 2014
Highschool
Gymnasium Carolinum Bernburg

Awards & Grants

SIGIR Student Travel Grant of the Special Interest Group on Information Retrieval (SIGIR) for the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020) for the paper Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain. (Citation: Gienapp, Stein et al., 2020 Gienapp, Stein et al. (2020). Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain. ACM . )
SIGIR Student Travel Grant of the Special Interest Group on Information Retrieval (SIGIR) for the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) for the paper Argument Search: Assessing Argument Relevance. (Citation: Potthast, Gienapp et al., 2019 Potthast, Gienapp et al. (2019). Argument Search: Assessing Argument Relevance. ACM . )

Publications

Elstner, T., Loebe, F., Ajjour, Y., Akiki, C., Bondarenko, A., Fröbe, M., Gienapp, L., Kolyada, N., Mohr, J., Sandfuchs, S., Wiegmann, M., Frochte, J., Ferro, N., Hofmann, S., Stein, B., Hagen, M. & Potthast, M. (2023). Shared Tasks as Tutorials: A Methodical Approach. EAAI .
Reimer, J., Schmidt, S., Fröbe, M., Gienapp, L., Scells, H., Stein, B., Hagen, M. & Potthast, M. (2023). The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives. ACM .
Bevendorff, J., Sauer, P., Gienapp, L., Kircheis, W., Körner, E., Stein, B. & Potthast, M. (2023). SMAuC – The Scientific Multi-Authorship Corpus.
Fröbe, M., Gienapp, L., Potthast, M. & Hagen, M. (2023). Bootstrapped nDCG Estimation in the Presence of Unjudged Documents. Springer .
Gienapp, L., Kircheis, W., Sievers, B., Stein, B. & Potthast, M. (2023). A large dataset of scientific text reuse in Open-Access publications. Scientific Data, 10(1).
Bondarenko, A., Fröbe, M., Gienapp, L., Pugachev, A., Reimer, J., Schlatt, F., Artemova, E., Potthast, M., Stein, B., Braslavski, P. & Hagen, M. (2022). Webis at TREC 2022: Deep Learning and Health Misinformation. National Institute of Standards; Technology (NIST) .
Gienapp, L., Fröbe, M., Hagen, M. & Potthast, M. (2022). Sparse Pairwise Re-ranking with Pre-trained Transformers. ACM .
Akiki, C., Gienapp, L. & Potthast, M. (2022). Tracking Discourse Influence in Darknet Forums. CoRR, abs/2202.02081.
Fröbe, M., Hagen, M., Bevendorff, J., Völske, M., Stein, B., Schröder, C., Wagner, R., Gienapp, L. & Potthast, M. (2021). The Impact of Main Content Extraction on Near-Duplicate Detection. International Open Search Symposium .
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M. & Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval. Springer .
Fröbe, M., Bevendorff, J., Gienapp, L., Völske, M., Stein, B., Potthast, M. & Hagen, M. (2021). CopyCat: Near-Duplicates within and between the ClueWeb and the Common Crawl. ACM .
Gienapp, L., Stein, B., Hagen, M. & Potthast, M. (2020). Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain. ACM .
Gienapp, L., Fröbe, M., Hagen, M. & Potthast, M. (2020). The Impact of Negative Relevance Judgments on NDCG. ACM .
Bondarenko, A., Fröbe, M., Beloucif, M., Gienapp, L., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M. & Hagen, M. (2020). Overview of Touché 2020: Argument Retrieval. Springer .
Gienapp, L., Stein, B., Hagen, M. & Potthast, M. (2020). Efficient Pairwise Annotation of Argument Quality. Association for Computational Linguistics .
Potthast, M., Gienapp, L., Euchner, F., Heilenkötter, N., Weidmann, N., Wachsmuth, H., Stein, B. & Hagen, M. (2019). Argument Search: Assessing Argument Relevance. ACM .