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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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Text Representations for Patent Classification
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In: http://wing.comp.nus.edu.sg/~antho/J/J13/J13-3009.pdf (2013)
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Constructing a broad coverage lexicon for text mining in the patent domain
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In: http://www.lrec-conf.org/proceedings/lrec2010/pdf/378_Paper.pdf (2010)
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Constructing a broad-coverage lexicon for text mining in the patent domain
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In: http://lands.let.kun.nl/literature/oostdijk.2010.4.pdf (2010)
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Quantifying the Challenges in Parsing Patent Claims
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In: http://lands.let.kun.nl/literature/sverbern.2010.1.pdf (2010)
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Evaluating paragraph retrieval for why-QA
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In: http://lands.let.kun.nl/literature/sverbern.2008.1.pdf (2008)
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Exploring the use of linguistic analysis for answering whyquestions
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In: http://lands.let.kun.nl/literature/sverbern.2006.3.pdf
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Using skipgrams and PoS-based feature selection for patent classification
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In: http://www.clinjournal.org/sites/default/files/4Dhondt2012_0.pdf
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Features for automatic discourse analysis of paragraphs
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In: http://lands.let.kun.nl/literature/daphne.2009.1.pdf
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Patent classification experiments with the Linguistic Classification System LCS
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In: http://clef2010.org/resources/proceedings/clef2010labs_submission_49.pdf
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How does the Library Searcher behave? A contrastive study of library search against ad-hoc search
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In: http://clef2010.org/resources/proceedings/clef2010labs_submission_42.pdf
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Information Foraging Lab
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In: http://ceur-ws.org/Vol-1177/CLEF2011wn-CLEF-IP-VerberneEt2011.pdf
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General Terms Design
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In: http://lands.let.kun.nl/literature/sverbern.2007.1.pdf
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Noname manuscript No. (will be inserted by the editor) Learning to Rank for Why-Question Answering
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In: http://lands.let.kun.nl/literature/sverbern.2011.1.pdf
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Phrase-based Document Categorization
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In: http://www.cs.kun.nl/%7Ekees/home/papers/PBDC-chapter.pdf
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Retrieval-based Question Answering for Machine Reading Evaluation
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In: http://ceur-ws.org/Vol-1177/CLEF2011wn-QA4MRE-Verberne2011.pdf
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Learning to Rank QA Data Evaluating Machine Learning Techniques for Ranking Answers to Why-Questions
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In: http://lands.let.kun.nl/literature/sverbern.2009.6.pdf
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Abstract:
In this work, we evaluate a number of machine learning techniques for the purpose of ranking answers to why-questions. We use a set of 37 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques in various settings. The purpose of the experiments is to assess how the different machine learning techniques can cope with our highly imbalanced binary relevance data. We find that with all machine learning techniques, we eventually obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. Regression techniques seem the best option for our learning problem. 1.
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URL: http://lands.let.kun.nl/literature/sverbern.2009.6.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.3355
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