PoS-tagging and lemmatization with a deep recurrent neural network
Neural networks have been shown to successfully solve many natural language processing tasks previously tackled by rule-based and statistical approaches. We present a deep recurrent network with long short-term memory, identical to engines used in machine translation, to solve the problem of joint P...
Elmentve itt :
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
2019
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| Sorozat: | Magyar Számítógépes Nyelvészeti Konferencia
15 |
| Kulcsszavak: | Nyelvészet - számítógép alkalmazása |
| Online Access: | http://acta.bibl.u-szeged.hu/59087 |
| Tartalmi kivonat: | Neural networks have been shown to successfully solve many natural language processing tasks previously tackled by rule-based and statistical approaches. We present a deep recurrent network with long short-term memory, identical to engines used in machine translation, to solve the problem of joint PoS-tagging and lemmatization in Hungarian and German. Our model achieves comparable or superior results to a state-of-the-art statistical PoS tagger. We are able to enhance the Hungarian model’s performance, as measured on a manually annotated sample unrelated to the initial training corpus, through an additional synthesized dataset. |
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| Terjedelem/Fizikai jellemzők: | 215-224 |
| ISBN: | 978-963-315-393-2 |