Suchergebnisse - "[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]"

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  1. 1

    Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

    Autoren: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

    Weitere Verfasser: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

    Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2674-2678

    Schlagwörter: FOS: Computer and information sciences, H.3, Rational Speech Acts, Computer Science - Computation and Language, 68P20, 68T50, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Neural Information Retrieval, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Computer Science - Information Retrieval, Sparse Retrieval, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Pragmatic Reasoning, Linguistic Pragmatics for Retrieval, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), Information Retrieval (cs.IR)

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2505.03676
    https://hal.science/hal-05074220v1
    https://hal.science/hal-05074220v1/document
    https://doi.org/10.1145/3726302.3730239

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  2. 2

    Towards Lossless Token Pruning in Late-Interaction Retrieval Models

    Autoren: Yuxuan Zong Benjamin Piwowarski ZONG, Yuxuan

    Weitere Verfasser: Yuxuan Zong Benjamin Piwowarski ZONG, Yuxuan

    Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2407-2417

    Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, CCS Concepts, Multi-vector Retrieval, Computer Science - Computation and Language, Efficiency-Effectiveness Trade-off, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, CCS Concepts Information systems → Document representation Retrieval models and ranking Information retrieval Information Retrieval, Artificial Intelligence (cs.AI), [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Information retrieval Information Retrieval, Dense Retrieval, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Information systems → Document representation, Retrieval models and ranking, Computation and Language (cs.CL), Information Retrieval (cs.IR)

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2504.12778

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  3. 3

    First High-Level Information Fusion Competition: Feedback and Lessons Learned

    Autoren: Laudy, Claire Alonso, Victoria Reverdy, Céline et al.

    Weitere Verfasser: Laudy, Claire Alonso, Victoria Reverdy, Céline et al.

    Quelle: 2025 28th International Conference on Information Fusion (FUSION). :1-8

    Schlagwörter: Knowledge graph, Competition, Ontology, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Information fusion, [INFO] Computer Science [cs], OWL

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://pasteur.hal.science/pasteur-05121854v1

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  4. 4

    Mining Faster, Not Harder: A New Criterion for Gradual Pattern Mining

    Autoren: Kamga Nguifo, Durande Lonlac, Jerry Fleury, Anthony et al.

    Weitere Verfasser: Kamga Nguifo, Durande Lonlac, Jerry Fleury, Anthony et al.

    Quelle: 2025 IEEE International Conference on Fuzzy Systems (FUZZ). :1-6

    Schlagwörter: gradual pattern, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], optimization, pattern mining

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://hal.science/hal-05163278v1

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  5. 5

    PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction

    Autoren: Poesina, Eduard Costache, Adriana Chifu, Adrian-Gabriel et al.

    Weitere Verfasser: Poesina, Eduard Costache, Adriana Chifu, Adrian-Gabriel et al.

    Quelle: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :28651-28661

    Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG)

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2406.04746
    https://hal.science/hal-05149275v1

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  6. 6

    Cool-Tee: Client-Tee Collaboration for Resilient Distributed Search

    Autoren: Bettinger, Matthieu Riviere, Etienne Mokhtar, Sonia Ben et al.

    Weitere Verfasser: Bettinger, Matthieu Riviere, Etienne Mokhtar, Sonia Ben et al.

    Quelle: 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). :353-362

    Schlagwörter: FOS: Computer and information sciences, distributed search, Trusted Execution Environments (TEE), marketplace, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2503.19063
    https://hdl.handle.net/2078.1/303797

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  7. 7

    Navigating the participatory turn in agricultural and food research: Best practice from citizen science

    Autoren: Ajates, Raquel Benyei, Petra Avery, Helen et al.

    Weitere Verfasser: Ajates, Raquel Benyei, Petra Avery, Helen et al.

    Quelle: Ambio
    Digital.CSIC. Repositorio Institucional del CSIC
    Consejo Superior de Investigaciones Científicas (CSIC)

    Schlagwörter: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, food systems, Agricultural research, [SHS.HISPHILSO] Humanities and Social Sciences/History, Philosophy and Sociology of Sciences, ecsa principles, [SDE.ES] Environmental Sciences/Environment and Society, participation, ECSA principles, [SDV.SA.AEP]Life Sciences [q-bio]/Agricultural sciences/Agriculture, economy and politics, agriculture, Ethics, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, [SHS.HISPHILSO]Humanities and Social Sciences/History, Philosophy and Sociology of Sciences, [SDV.SA.AEP] Life Sciences [q-bio]/Agricultural sciences/Agriculture, economy and politics, Participation, Agriculture, ethics, Food systems, Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Perspective, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], methodologies, Methodologies, Responsible Consumption and Production

    Dateibeschreibung: application/pdf; text

    Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39982654
    http://hdl.handle.net/10261/388414
    https://pure.iiasa.ac.at/id/eprint/20419/
    https://hal.inrae.fr/hal-04972056v1/document
    https://doi.org/10.1007/s13280-025-02151-7
    https://hal.inrae.fr/hal-04972056v1

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  8. 8

    DISPUTool 3.0: Fallacy Detection and Repairing in Argumentative Political Debates

    Autoren: Goffredo, Pierpaolo Dore, Deborah Cabrio, Elena et al.

    Weitere Verfasser: Goffredo, Pierpaolo Dore, Deborah Cabrio, Elena et al.

    Quelle: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). :472-480

    Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://hal.science/hal-05210504v1
    https://hal.science/hal-05210504v1/document
    https://doi.org/10.18653/v1/2025.acl-demo.45

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  9. 9

    Leveraging Graph Structural Knowledge to Improve Argument Relation Prediction in Political Debates

    Autoren: Dore, Deborah Faralli, Stefano Villata, Serena et al.

    Weitere Verfasser: Dore, Deborah Faralli, Stefano Villata, Serena et al.

    Quelle: Proceedings of the 12th Argument mining Workshop. :74-86

    Schlagwörter: [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]

    Dateibeschreibung: application/pdf

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  10. 10

    Categorization of scientometric data in a Benfordian context

    Autoren: Bertin, Marc Lafouge, Thierry

    Weitere Verfasser: Bertin, Marc Lafouge, Thierry

    Quelle: Quantitative Science Studies. 6:524-545

    Schlagwörter: MAX test, Generalized Benford's law, Zipf's law, Scientometrics, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], MAD (Mean Absolute Deviation), [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Benford's law, [SHS.INFO] Humanities and Social Sciences/Library and information sciences

    Zugangs-URL: https://hal.science/hal-05008569v1
    https://doi.org/10.1162/qss_a_00361

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  11. 11

    The Persuasive Impact of Text and Audio Features on Technology Crowdfunding Campaigns: An Empirical Study on Kickstarter

    Autoren: Dimitrios Dousios Nikolaos Korfiatis Konstantina Spanaki et al.

    Weitere Verfasser: Dimitrios Dousios Nikolaos Korfiatis Konstantina Spanaki et al.

    Quelle: IEEE Transactions on Engineering Management. 72:2049-2064

    Schlagwörter: Technology Crowdfunding, Feature Engineering, Text Features, Audio Features, Persuasion, Kickstarter, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], [SHS.INFO] Humanities and Social Sciences/Library and information sciences

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://ueaeprints.uea.ac.uk/id/eprint/98753/
    https://hal.science/hal-05122437v1/document
    https://hal.science/hal-05122437v1
    https://doi.org/10.1109/tem.2025.3549597

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  12. 12

    A lexicon obtained and validated by a data-driven approach for organic residues valorization in emerging and developing countries

    Autoren: Rakotomalala, Christiane Paillat, Jean-Marie Feder, Frédéric et al.

    Weitere Verfasser: Rakotomalala, Christiane Paillat, Jean-Marie Feder, Frédéric et al.

    Quelle: Frontiers in Artificial Intelligence

    Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, déchet organique, text mining, Computer Science - Information Retrieval, développement biologique, pays en développement, biological transformation, déchet agricole, valorization, agriculture, organic waste, 2. Zero hunger, Computer Science - Computation and Language, Agriculture, fouille de textes, organisation du travail, 15. Life on land, développement agricole, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), Information Retrieval (cs.IR)

    Dateibeschreibung: text; application/pdf

    Zugangs-URL: http://arxiv.org/abs/2406.00682
    http://agritrop.cirad.fr/614533/1/frai-1-1557137.pdf
    http://agritrop.cirad.fr/614533/
    https://hal.science/hal-05251071v1/document
    https://doi.org/10.3389/frai.2025.1557137
    https://hal.science/hal-05251071v1

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  13. 13

    Phenotypes Extraction from Text: Analysis and Perspective in the LLM Era

    Autoren: Baddour, Moussa Paquelet, Stéphane Rollier, Paul et al.

    Weitere Verfasser: Baddour, Moussa Paquelet, Stéphane Rollier, Paul et al.

    Quelle: 2024 IEEE 12th International Conference on Intelligent Systems (IS). :1-8

    Schlagwörter: LLM, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], phenotype, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, phenoBERT, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], genetic, entity linking, embeddings

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://inria.hal.science/hal-04647016v1/document
    https://inria.hal.science/hal-04647016v1
    https://doi.org/10.1109/is61756.2024.10705235

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  14. 14

    Equivariance-Based Self-Supervised Learning for Audio Signal Recovery from Clipped Measurements

    Autoren: Sechaud, Victor Jacques, Laurent Abry, Patrice et al.

    Weitere Verfasser: Sechaud, Victor Jacques, Laurent Abry, Patrice et al.

    Quelle: Proc of 32nd European Signal Processing Conference (EUSIPCO), Vol. 1, no.1, p. 852 (2024)

    Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], unsupervised learning, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Computer Science - Sound, Computer Science - Information Retrieval, Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), signal declipping, self-supervised learning, FOS: Electrical engineering, electronic engineering, information engineering, equivariance, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Electrical Engineering and Systems Science - Signal Processing, Information Retrieval (cs.IR), Electrical Engineering and Systems Science - Audio and Speech Processing

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2409.15283
    https://hdl.handle.net/2078.1/292968

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  15. 15

    Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders

    Autoren: Mathias Vast Basile Van Cooten Laure Soulier et al.

    Weitere Verfasser: Mathias Vast Basile Van Cooten Laure Soulier et al.

    Quelle: Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval. :133-143

    Schlagwörter: FOS: Computer and information sciences, Cross-Encoders, Information Retrieval, Integrated Gradients, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Explainability, Information Retrieval (cs.IR), Computer Science - Information Retrieval

    Zugangs-URL: http://arxiv.org/abs/2406.19309
    https://hal.sorbonne-universite.fr/hal-04668348v1
    https://doi.org/10.1145/3664190.3672528

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  16. 16

    BiodivPortal: Enabling Semantic Services for Biodiversity within the German National Research Data Infrastructure

    Autoren: Karam, Naouel Fillies, Jan Jonquet, Clement et al.

    Weitere Verfasser: Karam, Naouel Fillies, Jan Jonquet, Clement et al.

    Quelle: Datenbank-Spektrum. 24:129-137

    Schlagwörter: Data Integration, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Research Data Infrastructure, [INFO.INFO-WB] Computer Science [cs]/Web, [INFO.INFO-WB]Computer Science [cs]/Web, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Terminology Service, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data Harmonization, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Ontologies, Terminology Repository, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Taxonomies

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://hal.science/hal-04608953v1
    https://doi.org/10.1007/s13222-024-00474-5
    https://hal.science/hal-04608953v1/document

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  17. 17

    Report on the 19th French Conference on Information Retrieval and Applications (CORIA 2024)

    Autoren: Ermakova, Liana Mulhem, Philippe Doucet, Antoine et al.

    Weitere Verfasser: Ermakova, Liana Mulhem, Philippe Doucet, Antoine et al.

    Quelle: ACM SIGIR Forum. 58:1-4

    Schlagwörter: [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], 02 engineering and technology, 0509 other social sciences

    Dateibeschreibung: application/pdf

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  18. 18

    A Fast Plan Enumerator for Recursive Queries

    Autoren: Fejza, Amela Genevès, Pierre Layaïda, Nabil et al.

    Weitere Verfasser: Fejza, Amela Genevès, Pierre Layaïda, Nabil et al.

    Quelle: 2024 IEEE 40th International Conference on Data Engineering (ICDE). :5449-5452

    Schlagwörter: [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]

    Dateibeschreibung: application/pdf

    Zugangs-URL: https://hal.science/hal-04578576v1

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  19. 19

    A survey on SPARQL query relaxation under the lens of RDF reification

    Autoren: Fakih, Ginwa Serrano-Alvarado, Patricia Laboratoire des Sciences du Numérique de Nantes (LS2N) et al.

    Weitere Verfasser: Fakih, Ginwa Serrano-Alvarado, Patricia Laboratoire des Sciences du Numérique de Nantes (LS2N) et al.

    Quelle: Semantic Web. :1-48

    Schlagwörter: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], statement-level annotations, [INFO.INFO-WB]Computer Science [cs]/Web, ontology-based relaxation, query ranking, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], SPARQL query relaxation, similarity of instances, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], query similarity, [INFO]Computer Science [cs], RDF datasets, RDF reification models

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  20. 20

    Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

    Autoren: Yacine Belal Sonia Ben Mokhtar Hamed Haddadi et al.

    Weitere Verfasser: Yacine Belal Sonia Ben Mokhtar Hamed Haddadi et al.

    Quelle: ACM Transactions on Spatial Algorithms and Systems. 10:1-39

    Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, Mobility Prediction, Computer Science - Artificial Intelligence, 02 engineering and technology, Computer Science - Information Retrieval, Machine Learning (cs.LG), H.4.3, H.5.6, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], 0202 electrical engineering, electronic engineering, information engineering, Social and Information Networks (cs.SI), I.2.6, I.5.3, [INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], A.1, D.4.6, I.5.8, Computer Science - Social and Information Networks, Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Location Based Social Networks, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Distributed, Parallel, and Cluster Computing (cs.DC), Spatio-Temporal Applications, Federated Learning, Information Retrieval (cs.IR)

    Dateibeschreibung: application/pdf

    Zugangs-URL: http://arxiv.org/abs/2305.05257
    https://hal.science/hal-04613220v1/document
    https://doi.org/10.1145/3666089
    https://hal.science/hal-04613220v1

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