New word analogy corpus for exploring embeddings of Czech words

By Lukás Svoboda, Tomas Brychcin. 2016

21 citations recorded by the source corpus.

The word embedding methods have been proven to be very useful in many tasks of NLP (Natural Language Processing). Much has been investigated about word embeddings of English words and phrases, but only little attention has been dedicated to other languages.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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

1. Armand Joulin
2. Edouard Grave
3. Piotr Bojanowski
4. Prakhar Gupta
5. Douwe Kiela
6. Benoît Sagot
7. Luke S. Zettlemoyer
8. Jason Aaron Edward Weston

In Evaluation & Benchmarks

1. Edouard Grave
2. Prakhar Gupta
3. Tomas Mikolov
4. Armand Joulin
5. Piotr Bojanowski
6. Luke S. Zettlemoyer

Citations in the corpus

• Learning Word Vectors for 157 Languages (2018, methodology): “In our evaluation, we use the dataset of Svoboda and Brychcin (2016) for Czech, that of K¨oper et al. (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…”