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Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results.

K. Krieg, E. Parada-Cabaleiro, G. Medicus, O. Lesota, M. Schedl, and N. Rekabsaz. CoRR, (2022)

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Navid Tavakoli University of Stuttgart

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Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation.N. Rekabsaz, S. Sabetghadam, M. Lupu, L. Andersson, and A. Hanbury. LREC, European Language Resources Association (ELRA), (2016)Exploration of a Threshold for Similarity Based on Uncertainty in Word Embedding.N. Rekabsaz, M. Lupu, and A. Hanbury. ECIR, volume 10193 of Lecture Notes in Computer Science, page 396-409. (2017)Toward Optimized Multimodal Concept Indexing.N. Rekabsaz, R. Bierig, M. Lupu, and A. Hanbury. Trans. Comput. Collect. Intell., (2017)Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results.K. Krieg, E. Parada-Cabaleiro, G. Medicus, O. Lesota, M. Schedl, and N. Rekabsaz. CoRR, (2022)A Real-World Framework for Translator as Expert Retrieval.N. Rekabsaz, and M. Lupu. CLEF, volume 8685 of Lecture Notes in Computer Science, page 141-152. Springer, (2014)Enhancing Information Retrieval with Adapted Word Embedding.N. Rekabsaz. SIGIR, page 1169. ACM, (2016)On the Effect of Low-Frequency Terms on Neural-IR Models.S. Hofstätter, N. Rekabsaz, C. Eickhoff, and A. Hanbury. SIGIR, page 1137-1140. ACM, (2019)Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains.N. Rekabsaz, N. Pappas, J. Henderson, B. Khonglah, and S. Madikeri. CoRR, (2019)Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models.N. Rekabsaz, M. Lupu, A. Baklanov, A. Dür, L. Andersson, and A. Hanbury. ACL (1), page 1712-1721. Association for Computational Linguistics, (2017)On the use of statistical semantics for metadata-based social image retrieval.N. Rekabsaz, R. Bierig, B. Ionescu, A. Hanbury, and M. Lupu. CBMI, page 1-4. IEEE, (2015)

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Disambiguation of "Rekabsaz, Navid"

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Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results.

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Navid Tavakoli University of Stuttgart

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results.

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Navid Tavakoli University of Stuttgart

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Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results.