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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents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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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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Abstract:
In this paper, we aim to verify and quantify the challenges of patent claim processing that have been identified in the literature. We focus on the following three challenges that, judging from the numbers of mentions in papers concerning patent analysis and patent retrieval, are central to patent claim processing: (1) The length of sentences is much longer than for general language use; (2) Many novel terms are introduced in patent claims that are difficult to understand; (3) The syntactic structure of patent claims is complex. We find that the challenges of patent claim processing that are related to syntactic structure are much more problematic than the challenges at the vocabulary level. The sentence length issue only causes problems indirectly by resulting in more structural ambiguities for longer noun phrases.
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Keyword:
Challenges in Patent Search; Syntactic Parsing; Vocabulary Issues
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.181.970
http://lands.let.kun.nl/literature/sverbern.2010.1.pdf
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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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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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| 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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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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| 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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