Ugray Gábor: PoS-tagging and lemmatization with a deep recurrent neural network.
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Abstract
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Journal or Publication Title: | Magyar Számítógépes Nyelvészeti Konferencia |
| Date: | 2019 |
| Volume: | 15 |
| ISBN: | 978-963-315-393-2 |
| Page Range: | pp. 215-224 |
| Event Title: | Magyar Számítógépes Nyelvészeti Konferencia (15.) (2019) (Szeged) |
| Related URLs: | http://acta.bibl.u-szeged.hu/58556/ |
| Uncontrolled Keywords: | Nyelvészet - számítógép alkalmazása |
| Additional Information: | Bibliogr.: p. 223-224. ; összefoglalás angol nyelven |
| Date Deposited: | 2019. Jul. 03. 13:25 |
| Last Modified: | 2022. Nov. 08. 11:49 |
| URI: | http://acta.bibl.u-szeged.hu/id/eprint/59087 |
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