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Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 12th International C K. Selçuk Candan,Bogdan Ionescu,Nicola Ferro Co

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发表于 2025-3-21 17:17:08 | 显示全部楼层 |阅读模式
书目名称Experimental IR Meets Multilinguality, Multimodality, and Interaction
副标题12th International C
编辑K. Selçuk Candan,Bogdan Ionescu,Nicola Ferro
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 12th International C K. Selçuk Candan,Bogdan Ionescu,Nicola Ferro Co
描述This book constitutes the refereed proceedings of the 12th International Conference of the CLEF Association, CLEF 2021, held virtually in September 2021..The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data...The 11 full papers presented in this volume were carefully reviewed and selected from 21 submissions. This year, the contributions addressed the following challenges: application of neural methods for entity recognition as well as misinformation detection in the health area, skills extraction in job-match databases, stock market prediction using financial news, and extraction of audio features for podcast retrieval...In addition to this, the volume presents 5 “best of the labs” papers which were reviewed as full paper submissions with the same review criteria. 12 lab overview papers were accepted and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access..
出版日期Conference proceedings 2021
关键词artificial intelligence; computational linguistics; computer vision; data mining; databases; hci; human-co
版次1
doihttps://doi.org/10.1007/978-3-030-85251-1
isbn_softcover978-3-030-85250-4
isbn_ebook978-3-030-85251-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Comparing Traditional and Neural Approaches for Detecting Health-Related Misinformationformation that is difficult to read. Our results suggest that traditional models are still a strong baseline for these challenging tasks. In the absence of substantive training data, classical approaches tend to outperform BERT-based models.
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Angela R. Starkweather,Susan G. Dorseyt focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using
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Anton G. Kutikhin,Arseniy E. Yuzhalinformation that is difficult to read. Our results suggest that traditional models are still a strong baseline for these challenging tasks. In the absence of substantive training data, classical approaches tend to outperform BERT-based models.
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Sivakumar Sukumaran,Jianming Yuve rounds of growing topics, documents and relevance judgments. The results of our experiments show that the pivot strategy can propose a correct ranking of systems evaluated in an evolving test collection.
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