Word Embeddings Go to Italy: A Comparison of Models and Training Datasets
By Giacomo Berardi, Andrea Esuli, Diego Marcheggiani. 2015
52 citations recorded by the source corpus.
Subfields: Natural Language Processing, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Armand Joulin
- Edouard Grave
- Piotr Bojanowski
- Prakhar Gupta
- Douwe Kiela
- Benoît Sagot
- Luke S. Zettlemoyer
- Jason Aaron Edward Weston
In Representation & Self-Supervised Learning
- Armand Joulin
- Piotr Bojanowski
- Edouard Grave
- Prakhar Gupta
- Tomas Mikolov
- Hinrich Schütze
- Benoît Sagot
- Pedro Ortiz Suarez
Citations in the corpus
- Learning Word Vectors for 157 Languages (2018, background): “(2015) for German, that of Cardellino (2016) for Spanish, that of Venekoski and Vankka (2017) for Finnish, that of Berardi et al. (2015) for Italian, the European variant of the dataset proposed by Hartmann et al.”
- Enriching Word Vectors with Subword Information (2016, methodology): “We use the datasets introduced by Mikolov et al. (2013a) for English, by Svoboda and Brychcin (2016) for Czech, by Köper et al. (2015) for German and by Berardi et al. (2015) for Italian.”