2023S

• Advanced Information Retrieval / 188.980 / VU

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

• Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction / Hofstätter, S., Khattab, O., Althammer, S., Sertkan, M., & Hanbury, A. (2022). Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction. In CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 737–747). Association for Computing Machinery (ACM). https://doi.org/10.1145/3511808.3557367 / Project: DoSSIER
• TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval / Althammer, S., Hofstätter, S., Verberne, S., & Hanbury, A. (2022). TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval. In CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 3801–3805). https://doi.org/10.1145/3511808.3557714
• Diversifying Sentiments in News Recommendation / Sertkan, M., Althammer, S., Hofstätter, S., & Neidhardt, J. (2022). Diversifying Sentiments in News Recommendation. In Perspectives 2022. Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022. PERSPECTIVES 2022 - Perspectives on the Evaluation of Recommender Systems Workshop co-located with the 16th ACM Conference on Recommender Systems, Seattle, WA, United States of America (the). https://doi.org/10.34726/3903 / Project: CDL-RecSys
• PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval / Althammer, S., Hofstätter, S., Sertkan, M., Verberne, S., & Hanbury, A. (2022). PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval. In Advances in Information Retrieval (pp. 19–34). Springer. https://doi.org/10.1007/978-3-030-99736-6_2
• DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval / Althammer, S., Askari, A., Verberne, S., & Hanbury, A. (2021). DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval. In Proceedings of the Eighth International Competition on Legal Information Extraction/Entailment (COLIEE) (pp. 8–14). http://hdl.handle.net/20.500.12708/58761
• Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study / Althammer, S., Hofstätter, S., & Hanbury, A. (2021). Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study. In Lecture Notes in Computer Science (pp. 3–17). https://doi.org/10.1007/978-3-030-72240-1_1
• Mitigating the Position Bias of Transformer Models in Passage Re-ranking / Hofstätter, S., Lipani, A., Althammer, S., Zlabinger, M., & Hanbury, A. (2021). Mitigating the Position Bias of Transformer Models in Passage Re-ranking. In Lecture Notes in Computer Science (pp. 238–253). https://doi.org/10.1007/978-3-030-72113-8_16
• Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation / Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M., & Hanbury, A. (2020). Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (p. 8). arXiv. http://hdl.handle.net/20.500.12708/141680