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Capitalizing on a TREC Track to Build a Tweet Summarization Dataset
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In: CIRCLE 2020 ; Proceedings of the Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) ; Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) ; https://hal.archives-ouvertes.fr/hal-03095613 ; Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020), Université de Toulouse, France, Jul 2020, Samatan, Gers, France. pp.1-9 ; http://ceur-ws.org/Vol-2621/CIRCLE20_20.pdf (2020)
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Prediction and Visual Intelligence for Security Information: The PREVISION H2020 Project
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In: CIRCLE 2020 ; https://hal.archives-ouvertes.fr/hal-02877780 ; CIRCLE 2020, Iván Cantador; Max Chevalier; Massimo Melucci; Josiane Mothe, Jul 2020, Samatan, France ; http://ceur-ws.org/Vol-2621/ (2020)
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Automatic Authoring and Construction of Hypermedia for Information Retrieval
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In: http://ir.dcs.gla.ac.uk/papers/Pdf/agosti+94a.pdf (1995)
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University of Padua at CLEF 2002: Experiments to evaluate a statistical stemming algorithm
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In: http://www.clef-campaign.org/workshop2002/WN/20.pdf
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Abstract:
In Information Retrieval (IR), stemming is used to reduce variant word forms to common root. The assumption is that if two words have the same root, then they represent the same concept. Hence stemming permits a IR system to match query and document terms which are related to a same meaning but which can appear in different morphological variants. In this paper we will report our participation in CLEF 2002 Italian monolingual task, whose aim was to evaluate a statistical stemming algorithm based on link analysis. Considering that a word is formed by a prefix (stem) and a suffix, the key idea is that the interlinked prefixes and suffixes form a community of substrings. Hence discovering these communities means searching for the best word splits which give the best word stems. The results show that stemming improves the IR effectiveness. They also show that effectiveness level of our algorithm, which does not incorporate any heuristics nor linguistic knowledge, is comparable to that of an algorithm based on a-priori linguistic knowledge. This is an encouraging result, particularly in a multi-lingual context. 1
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.4398
http://www.clef-campaign.org/workshop2002/WN/20.pdf
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Design, implementation, and evaluation of a methodology for automatic stemmer generation
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