Natural Language systems have evolved tremendously in the past few years from dealing only with small handcrafted examples to extremely large, real-world applications.
We have over a decade of experience in developing industrial-strength custom AI solutions. We specialize in Machine Learning & Natural Language Processing (NLP) solutions...
The Natural Language Decathlon (decaNLP) is a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
Catalogue des livres du 16e siècle de la Bibliothèque de l'Etat de Bavière [BSB] dans le cadre de VD16. En fait, on trouve aussi des incunables et des livres du 17e siècle (utiliser la recherche chronologique).
Bibliothèque virtuelle de manuscrits conservés dans les bibliothèques suisses (actuellement 243 manuscrits dans 14 collections, dont St. Gall, la Fondation Bodmer, etc.)
Educational Natural Language Processing (e-NLP) aims at
finding new applications of Natural Language Processing for educational purposes, and developing new techniques and software taking into account the specific needs in the educational domain.
We especially focus on NLP applications for eLearning 2.0, which is characterized by a worldwide learning community where educational material is produced both by students and teachers. This brings about new challenges for NLP since the amount of user-generated discourse and social media content such as wikis and blogs is constantly growing and requires intelligent automatic processing.
Extensible Dependency Grammar (XDG) is a general framework for dependency grammar, with multiple levels of linguistic representations called dimensions, e.g. grammatical function, word order, predicate-argument structure, scope structure, information structure and prosodic structure. It is articulated around a graph description language for multi-dimensional attributed labeled graphs.
An XDG grammar is a constraint that describes the valid linguistic signs as n-dimensional attributed labeled graphs, i.e. n-tuples of graphs sharing the same set of attributed nodes, but having different sets of labeled edges. All aspects of these signs are stipulated explicitly by principles: the class of models for each dimension, additional properties that they must satisfy, how one dimension must relate to another, and even lexicalization.
R. Swanson, und A. Gordon. Proceedings of the Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics, Seite 17-21. Sydney, Australia, (Juli 2006)
R. Swanson, und A. Gordon. Proceedings of the Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics, Seite 17-21. Sydney, Australia, (Juli 2006)
L. Rino, T. Pardo, C. Silla Jr., C. Kaestner, und M. Pombo. Proceedings of the 17th Brazilian Symposium on Artificial Intelligence (SBIA), Seite 235-244. São Luis-MA, Brazil, (September 2004)