Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Etcheverry, Mathias"'
Publikováno v:
ISSN 1613-0073, TASS 2017: Workshop on Semantic Analysis at SEPLN, Sep 2017, pages 77-83
This article presents classifiers based on SVM and Convolutional Neural Networks (CNN) for the TASS 2017 challenge on tweets sentiment analysis. The classifier with the best performance in general uses a combination of SVM and CNN. The use of word em
Externí odkaz:
http://arxiv.org/abs/1710.06393
Autor:
Rosá, Aiala, Chiruzzo, Luis, Bouza, Lucía, Dragonetti, Alina, Castro, Santiago, Etcheverry, Mathias, Góngora, Santiago, Goycoechea, Santiago, Machado, Juan, Moncecchi, Guillermo, Prada, Juan José, Wonsever, Dina
Publikováno v:
RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
Universidad de Alicante (UA)
We present the results of the QuALES task, which addresses the problem of Extractive Question Answering from texts. For both training and evaluation we use the QuALES corpus, a corpus of Uruguayan media news about the Covid-19 pandemic and related to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e520890cd23e8892ca35f753eac0b782
https://hdl.handle.net/10045/127433
https://hdl.handle.net/10045/127433
Publikováno v:
RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
Universidad de Alicante (UA)
We present different methods for Sentiment analysis in Spanish tweets: SVM based on word embeddings centroid for the tweet, CNN and LSTM.We analyze the results obtained using the corpora from the TASS sentiment analysis challenge, obtaining state of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::dc18e71f24ca6400d8b2bced452b3bc1
https://hdl.handle.net/10045/104719
https://hdl.handle.net/10045/104719
Autor:
Etcheverry, Mathias
Publikováno v:
COLIBRI
Universidad de la República
instacron:Universidad de la República
Universidad de la República
instacron:Universidad de la República
En esta tesis se realiza el reconocimiento y la clasificación de expresiones temporales en español sin incluir otra información explícita del dominio que los datos de entrenamiento. El enfoque propuesto consiste en modelos de redes neuronales art
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::b8884ddc00a3ceb1d3fe1c18e41b07eb
Autor:
Etcheverry, Mathias, Wonsever, Dina
This work re-examines the widely addressed problem of the recognition and interpretation of time expressions, and suggests an approach based on distributed representations and artificial neural networks. Artificial neural networks allow us to build h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61593ff45ef5e531938097d3e8cca974
Publikováno v:
Procesamiento del Lenguaje Natural; Jan-Mar2020, Issue 64, p109-116, 9p
Publikováno v:
Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial; 2019, Vol. 22 Issue 64, p1-13, 13p