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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 ABSTRACT Long Tail in Weighted Lexical Networks Mathieu Lafourcade; Alain Joubert In: http://hal.inria.fr/docs/00/81/62/36/PDF/COGALEX2012-ML-v4.pdf (2013) BASE Show details 2 TOTAKI: a help for lexical access on the TOT problem Mathieu Lafourcade Alain Joubert; Université Montpellier Ii In: http://www.lirmm.fr/%7Elafourcade/ML-biblio/fest/TOTAKI-TOT-v14-auth.pdf BASE Show details 3 Collecting and Evaluating Lexical Polarity with a Game With A Purpose Mathieu Lafourcade; Nathalie Le Brun; Alain Joubert In: http://www.lirmm.fr/%7Elafourca/ML-biblio/RANLP2015/RANLP2015NLBAJML-v14.pdf BASE Show details 4 Inductive and deductive inferences in a Crowdsourced Lexical-Semantic Network Manel Zarrouk; Alain Joubert In: http://www.lirmm.fr/~lafourcade/ML-biblio/RANLP2014/RANLP_2014_MZMLAJ.pdf BASE Show details 5 ABSTRACT Long Tail in Weighted Lexical Networks Mathieu Lafourcade; Alain Joubert In: http://www.aclweb.org/anthology/W/W12/W12-5102.pdf BASE Show details 6 ABSTRACT Long Tail in Weighted Lexical Networks Mathieu Lafourcade; Alain Joubert In: http://www.lirmm.fr/~lafourcade/ML-biblio/COGALEX3/COGALEX2012-ML-v4.pdf Abstract: Lexical networks can be used with benefit for semantic analysis of texts, word sense disambiguation (WSD) and in general for graph-based Natural Language Processing. Usually strong relations between terms (e.g.: cat--> animal) are sufficient to help for the task, but quite often, weak relations (e.g.: cat--> ball of wool) are necessary. Our purpose here is to acquire such relations by means of online serious games as other classical approaches seems impractical. Indeed, it is difficult to ask the users (non experts) to define a proper weighting for the relations they propose, and then we decided to relate weights with the frequency of their propositions. It allows us to acquire first the strongest relations, but also to populate the long tail of an already existing network. Furthermore, trying to get an estimation of our network by the very users thanks to a tip of the tongue (TOT) software, we realized that they rather tend to favor the relations of the long tail and thus promote their emergence. Developing the long tail of a lexical network with standard and non-standard relations of low weight can be of advantage for tasks such that words retrieval from clues or WSD in texts. Keyword: GAME WITH A PURPOSE; LEXICAL NETWORK; LONG TAIL; TIP OF THE TONGUE SOFTWARE; TYPED RELATIONS; WEIGHTED RELATIONS; WSD URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.9760 http://www.lirmm.fr/~lafourcade/ML-biblio/COGALEX3/COGALEX2012-ML-v4.pdf BASE Hide details

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