Comparison of distributed language models on medium-resourced languages
word2vec and GloVe are the two most successful open-source tools that compute distributed language models from gigaword corpora. word2vec implements the neural network style architectures skip-gram and cbow, learning parameters using each word as a training sample, while GloVe factorizes the cooccur...
Elmentve itt :
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| Testületi szerző: | |
| Dokumentumtípus: | Könyv része |
| Megjelent: |
2015
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| Sorozat: | Magyar Számítógépes Nyelvészeti Konferencia
11 |
| Kulcsszavak: | Nyelvészet - számítógép alkalmazása |
| Online Access: | http://acta.bibl.u-szeged.hu/58918 |
| Tartalmi kivonat: | word2vec and GloVe are the two most successful open-source tools that compute distributed language models from gigaword corpora. word2vec implements the neural network style architectures skip-gram and cbow, learning parameters using each word as a training sample, while GloVe factorizes the cooccurrence-matrix (or more precisely a matrix of conditional probabilities) as a whole. In the present work, we compare the two systems on two tasks: a Hungarian equivalent of a popular word analogy task and word translation between European languages including medium-resourced ones e.g. Hungarian, Lithuanian and Slovenian. |
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| Terjedelem/Fizikai jellemzők: | 22-33 |
| ISBN: | 978-963-306-359-0 |