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1
Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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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2
Text Representations for Patent Classification
In: http://wing.comp.nus.edu.sg/~antho/J/J13/J13-3009.pdf (2013)
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3
Constructing a broad coverage lexicon for text mining in the patent domain
In: http://www.lrec-conf.org/proceedings/lrec2010/pdf/378_Paper.pdf (2010)
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4
Constructing a broad-coverage lexicon for text mining in the patent domain
In: http://lands.let.kun.nl/literature/oostdijk.2010.4.pdf (2010)
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5
Quantifying the Challenges in Parsing Patent Claims
In: http://lands.let.kun.nl/literature/sverbern.2010.1.pdf (2010)
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6
Evaluating paragraph retrieval for why-QA
In: http://lands.let.kun.nl/literature/sverbern.2008.1.pdf (2008)
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7
Exploring the use of linguistic analysis for answering whyquestions
In: http://lands.let.kun.nl/literature/sverbern.2006.3.pdf
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8
Using skipgrams and PoS-based feature selection for patent classification
In: http://www.clinjournal.org/sites/default/files/4Dhondt2012_0.pdf
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9
Features for automatic discourse analysis of paragraphs
In: http://lands.let.kun.nl/literature/daphne.2009.1.pdf
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10
Patent classification experiments with the Linguistic Classification System LCS
In: http://clef2010.org/resources/proceedings/clef2010labs_submission_49.pdf
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11
How does the Library Searcher behave? A contrastive study of library search against ad-hoc search
In: http://clef2010.org/resources/proceedings/clef2010labs_submission_42.pdf
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12
Information Foraging Lab
In: http://ceur-ws.org/Vol-1177/CLEF2011wn-CLEF-IP-VerberneEt2011.pdf
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13
General Terms Design
In: http://lands.let.kun.nl/literature/sverbern.2007.1.pdf
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14
Noname manuscript No. (will be inserted by the editor) Learning to Rank for Why-Question Answering
In: http://lands.let.kun.nl/literature/sverbern.2011.1.pdf
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15
Phrase-based Document Categorization
In: http://www.cs.kun.nl/%7Ekees/home/papers/PBDC-chapter.pdf
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16
Retrieval-based Question Answering for Machine Reading Evaluation
In: http://ceur-ws.org/Vol-1177/CLEF2011wn-QA4MRE-Verberne2011.pdf
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17
Learning to Rank QA Data Evaluating Machine Learning Techniques for Ranking Answers to Why-Questions
In: http://lands.let.kun.nl/literature/sverbern.2009.6.pdf
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.
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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