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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents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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| 2 |
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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| 4 |
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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| 5 |
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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| 8 |
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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| 9 |
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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| 10 |
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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Abstract:
Abstract. In the context of the CLEF-IP 2010 classification task, we conducted a series of experiments with the Linguistic Classification System (LCS). We compared two document representations for patent abstracts: a bag-of-words representation and a syntactic/semantic representation containing both words and dependency triples. We evaluated two types of output: using a fixed cut-off on the ranking of the classes and using a flexible cut-off based on a threshold on the classification scores. Using the Winnow classifier, we obtained an improvement in classification scores when triples are added to the bag of words. However, our results are remarkably better on a held-out subset of the target data than on the 2 000-topic test set. The main findings of this paper are: (1) adding dependency triples to words has a positive effect on classification accuracy and (2) selecting classes by using a threshold on the classification scores instead of returning a fixed number of classes per document improves classification scores while at the same time it lowers the number of classes needs to be judged manually by the professionals at the patent office. 1
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URL: http://clef2010.org/resources/proceedings/clef2010labs_submission_49.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.3529
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| 11 |
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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| 12 |
Information Foraging Lab
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In: http://ceur-ws.org/Vol-1177/CLEF2011wn-CLEF-IP-VerberneEt2011.pdf
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| 13 |
General Terms Design
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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
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In: http://lands.let.kun.nl/literature/sverbern.2011.1.pdf
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| 15 |
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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