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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents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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Abstract:
For mining intellectual property texts (patents), a broad-coverage lexicon that covers general English words together with terminology from the patent domain is indispensable. The patent domain is very diffuse as it comprises a variety of technical domains (e.g. Human Necessities, Chemistry & Metallurgy and Physics in the International Patent Classification). As a result, collecting a lexicon that covers the language used in patent texts is not a straightforward task. In this paper we describe the approach that we have developed for the semi-automatic construction of a broad-coverage lexicon for classification and information retrieval in the patent domain and which combines information from multiple sources. Our contribution is twofold. First, we provide insight into the difficulties of developing lexical resources for information retrieval and text mining in the patent domain, a research and development field that is expanding quickly. Second, we create a broad coverage lexicon annotated with rich lexical information and containing both general English word forms and domain terminology for various technical domains.
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URL: http://www.lrec-conf.org/proceedings/lrec2010/pdf/378_Paper.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.6320
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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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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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| 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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| 16 |
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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| 17 |
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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