منابع مشابه
Algorithme automatique non supervisé pour le Deft 2012 (Automatic unsupervised algorithm for Deft 2012) [in French]
Murat Ahat 1 Coralie Petermann 1, 2 Yann Vigile Hoareau 3 Soufian Ben Amor 1 Marc Bui 2 (1) Prism, Université de Versailles Saint-Quentin-en-Yvelines, 35 avenue des Etats-Unis, F-78035 Versailles. (2) LaISC, Ecole Pratique des Hautes Etudes, 41 rue Gay-Lussac, F-75005 Paris. (3) CHArt, 41 rue Gay-Lussac, F-75005 Paris. [email protected], [email protected], hoareau@lutin-userlab...
متن کاملA Comparison of Event Representations in DEFT
This paper will discuss and compare event representations across a variety of types of event annotation: Rich Entities, Relations, and Events (Rich ERE), Light Entities, Relations, and Events (Light ERE), Event Nugget (EN), Event Argument Extraction (EAE), Richer Event Descriptions (RED), and Event-Event Relations (EER). Comparisons of event representations are presented, along with a compariso...
متن کاملSystèmes du LIA à DEFT'13
The Systems of LIA at DEFT’13 The 2013 Défi de Fouille de Textes (DEFT) campaign is interested in two types of language analysis tasks, the document classification and the information extraction in the specialized domain of cuisine recipes. We present the systems that the LIA has used in DEFT 2013. Our systems show interesting results, even though the complexity of the proposed tasks. MOTS-CLÉS...
متن کاملDocument Level Subjectivity Classification Experiments in DEFT’09 Challenge
Cet article présente nos expériences de classification supervisée pour la subjectivité au niveau des documents, pour l’anglais et pour le français, au cours du Défi DEFT’09 de fouille de textes. Nous avons testé des traits portant sur les mots, les parties du discours et sur des vocabulaires spécialisés pour faire fonctionner un classificateur SVM. Nos expériences sur les traits des mots examin...
متن کاملDeFT: A conceptual framework for considering learning with multiple representations
Multiple (external) representations can provide unique benefits when people are learning complex new ideas. Unfortunately, many studies have shown this promise is not always achieved. The DeFT (Design, Functions, Tasks) framework for learning with multiple representations integrates research on learning, the cognitive science of representation and constructivist theories of education. It propos...
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ژورنال
عنوان ژورنال: Nature
سال: 2001
ISSN: 0028-0836,1476-4687
DOI: 10.1038/35086694