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1
Word Sense Induction with Attentive Context Clustering
In: https://hal.archives-ouvertes.fr/hal-03586559 ; 2022 (2022)
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Word Sense Induction with Attentive Context Clustering
In: https://hal.inria.fr/hal-03586559 ; 2022 (2022)
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Word Sense Induction with Attentive Context Clustering
In: https://hal.archives-ouvertes.fr/hal-03586559 ; 2022 (2022)
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4
Event/Nonevent Noun Disambiguation Test Set ...
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Event/Nonevent Noun Disambiguation Test Set ...
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6
Russian Event Noun Disambiguation Test Set (6X200) ...
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7
Russian Event Noun Disambiguation Test Set (6X200) ...
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8
Multi-sense Embeddings through a Word Sense Disambiguation Process
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9
Crowdsourcing lexical semantic judgements from bilingual dictionary users
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10
Uso de representaciones vectoriales de las palabras para la detección de dobles sentidos (puns)
Carrasco Gómez, Pascual Andrés. - : Universitat Politècnica de València, 2017
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11
Unsupervised all-words sense distribution learning
Bennett, Andrew. - 2016
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12
Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical Resources
In: Cognitive Studies | Études cognitives; No 15 (2015); 269-292 ; 2392-2397 (2015)
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13
Translation of keywords between English and Swedish ; Översättning av nyckelord mellan engelska och svenska
Klein Rosmar, Sander; Ahmady, Tobias. - : KTH, Data- och elektroteknik, 2014
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14
ABSTRACT Long Tail in Weighted Lexical Networks
In: http://hal.inria.fr/docs/00/81/62/36/PDF/COGALEX2012-ML-v4.pdf (2013)
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://hal.inria.fr/docs/00/81/62/36/PDF/COGALEX2012-ML-v4.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.383.2346
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15
Désambiguisation de sens par modèles de contextes et son application à la Recherche d’Information
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16
Désambiguisation de sens par modèles de contextes et son application à la Recherche d’Information
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17
SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
In: Association for Computational Linguistics (ACL) Workshop on Semantic Evaluations (SemEval), 2010, Uppsala, Sweden (2010)
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18
Word sense disambiguation: a survey
In: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf (2009)
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19
Regular Polysemy in WordNet
In: ISSN: 0175-1336 ; Journal for language technology and computational linguistics ; https://hal.archives-ouvertes.fr/hal-00611244 ; Journal for language technology and computational linguistics, GSCL (Gesellschaft für Sprachtechnologie und Computerlinguistik) 2009, 24 (2), pp.5-18 (2009)
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20
The Treatment of Word Sense Inventories in the ‘LACELL WSD Project’
In: International Journal of English Studies; Vol. 9 No. 3 (2009): Special Issue; 21-38 ; International Journal of English Studies; Vol. 9 Núm. 3 (2009): Special Issue; 21-38 ; 1989-6131 ; 1578-7044 (2009)
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