no code implementations • WS 2019 • Ignatius Ezeani, Scott Piao, Steven Neale, Paul Rayson, Dawn Knight
While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models.
no code implementations • 14 Aug 2016 • Steven Neale, Valeria de Paiva, Arantxa Otegi, Alexandre Rademaker
Lexical semantics continues to play an important role in driving research directions in NLP, with the recognition and understanding of context becoming increasingly important in delivering successful outcomes in NLP tasks.
no code implementations • WS 2016 • Rosa Gaudio, Gorka Labaka, Eneko Agirre, Petya Osenova, Kiril Simov, Martin Popel, Dieke Oele, Gertjan van Noord, Lu{\'\i}s Gomes, Jo{\~a}o Ant{\'o}nio Rodrigues, Steven Neale, Jo{\~a}o Silva, Andreia Querido, Nuno Rendeiro, Ant{\'o}nio Branco
no code implementations • LREC 2016 • Arantxa Otegi, Nora Aranberri, Antonio Branco, Jan Haji{\v{c}}, Martin Popel, Kiril Simov, Eneko Agirre, Petya Osenova, Rita Pereira, Jo{\~a}o Silva, Steven Neale
This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference.
no code implementations • LREC 2016 • Steven Neale, Lu{\'\i}s Gomes, Eneko Agirre, Oier Lopez de Lacalle, Ant{\'o}nio Branco
Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question.