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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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents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
Abstract: In this paper, we investigate which information is useful for the detection of rhetorical (RST) relations between (Multi-) Sentential Discourse Units ((M-)SDUs)–text spans consisting of one or more sentences–within the same paragraph. In order to do so, we simplified the task of discourse parsing to a decision problem in which we decided whether an (M-)SDU is either rhetorically related to a preceding or a following (M-)SDU. Employing the RST Treebank (Carlson et al. 2003), we offered this choice to machine learning algorithms together with syntactic, lexical, referential, discourse and surface features. Next, the features were ranked on the basis of (1) models established by the classification algorithms and (2) feature selection metrics. Highly ranked features that predict the presence of a rhetorical relation are syntactic similarity, word overlap, word similarity, continuous punctuation and many reference features. Other features are used to introduce new topics or arguments: time references, proper nouns, definite articles and the word further. 1
URL: http://lands.let.kun.nl/literature/daphne.2009.1.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.1138
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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
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