no code implementations • GWC 2018 • Alfonso Methol, Guillermo López, Juan Álvarez, Luis Chiruzzo, Dina Wonsever
We present some strategies for improving the Spanish version of WordNet, part of the MCR, selecting new lemmas for the Spanish synsets by translating the lemmas of the corresponding English synsets.
no code implementations • GWC 2016 • Matias Herrera, Javier Gonzalez, Luis Chiruzzo, Dina Wonsever
Although there are currently several versions of Princeton WordNet for different languages, the lack of development of some of these versions does not make it possible to use them in different Natural Language Processing applications.
no code implementations • 29 Sep 2021 • Mathias Etcheverry, Dina Wonsever
Antonymic and synonymic pairs may both occur nearby in word embeddings spaces because they have similar distributional information.
no code implementations • WS 2020 • Luis Chiruzzo, Dina Wonsever
This paper presents the development of a deep parser for Spanish that uses a HPSG grammar and returns trees that contain both syntactic and semantic information.
no code implementations • LREC 2020 • Gun Woo Lee, Mathias Etcheverry, Fern, Daniel ez Sanchez, Dina Wonsever
This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word vectors as input.
no code implementations • 9 Oct 2019 • Diego Garat, Dina Wonsever
In many countries, personal information that can be published or shared between organizations is regulated and, therefore, documents must undergo a process of de-identification to eliminate or obfuscate confidential data.
no code implementations • ACL 2019 • Mathias Etcheverry, Dina Wonsever
Discriminating antonyms and synonyms is an important NLP task that has the difficulty that both, antonyms and synonyms, contains similar distributional information.
no code implementations • LREC 2016 • Dina Wonsever, Aiala Ros{\'a}, Marisa Malcuori
We present a proposal for the annotation of factuality of event mentions in Spanish texts and a free available annotated corpus.
no code implementations • LREC 2016 • Mathias Etcheverry, Dina Wonsever
Contents analisys from text data requires semantic representations that are difficult to obtain automatically, as they may require large handcrafted knowledge bases or manually annotated examples.