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Visual Disambiguation of Preprositional Phrase Attachments : Multimodal Machine Learning for Syntactic Analysis Correction
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In: Advances in Computational Intelligence 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019 ; IWANN: International Work-Conference on Artificial Neural Networks ; https://hal.archives-ouvertes.fr/hal-02465051 ; IWANN: International Work-Conference on Artificial Neural Networks, Jun 2019, Gran Canaria, Spain. ⟨10.1007/978-3-030-20521-8_52⟩ (2019)
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Correction automatique d'attachements prépositionnels par utilisation de traits visuels
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In: 25ème conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-01832979 ; 25ème conférence sur le Traitement Automatique des Langues Naturelles, May 2018, Rennes, France (2018)
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Relational Grounded Language Learning
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In: ECAI: European Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-01386945 ; ECAI: European Conference on Artificial Intelligence, Aug 2016, The Hague, Netherlands. pp.1764-1765 (2016)
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Abstract:
International audience ; In the past, research on learning language models mainly used syntactic information during the learning process but in recent years, researchers began to also use semantic information. This paper presents such an approach where the input of our learning algorithm is a dataset of pairs made up of sentences and the contexts in which they are produced. The system we present is based on inductive logic programming techniques that aim to learn a mapping between n-grams and a semantic representation of their associated meaning. Experiments have shown that we can learn such a mapping that made it possible later to generate relevant descriptions of images or learn the meaning of words without any linguistic resource.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]
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URL: https://hal.archives-ouvertes.fr/hal-01386945
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Linguistic Models at the Crossroads of Agents, Learning and Formal Languages
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In: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal; Vol. 3 No. 4 (2014); 67-87 ; ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal; Vol. 3 Núm. 4 (2014); 67-87 ; 2255-2863 ; 10.14201/ADCAIJ201434 (2014)
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On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries
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In: TDX (Tesis Doctorals en Xarxa) (2006)
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