Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929501157457920 |
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| author | Straka, Milan Straková, Jana |
| author_facet | Straka, Milan Straková, Jana |
| contents | We present an open-source web service for Czech morphosyntactic analysis. The system combines a deep learning model with rescoring by a high-precision morphological dictionary at inference time. We show that our hybrid method surpasses two competitive baselines: While the deep learning model ensures generalization for out-of-vocabulary words and better disambiguation, an improvement over an existing morphological analyser MorphoDiTa, at the same time, the deep learning model benefits from inference-time guidance of a manually curated morphological dictionary. We achieve 50% error reduction in lemmatization and 58% error reduction in POS tagging over MorphoDiTa, while also offering dependency parsing. The model is trained on one of the currently largest Czech morphosyntactic corpora, the PDT-C 1.0, with the trained models available at https://hdl.handle.net/11234/1-5293. We provide the tool as a web service deployed at https://lindat.mff.cuni.cz/services/udpipe/. The source code is available at GitHub (https://github.com/ufal/udpipe/tree/udpipe-2), along with a Python client for a simple use. The documentation for the models can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12422 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech Straka, Milan Straková, Jana Computation and Language We present an open-source web service for Czech morphosyntactic analysis. The system combines a deep learning model with rescoring by a high-precision morphological dictionary at inference time. We show that our hybrid method surpasses two competitive baselines: While the deep learning model ensures generalization for out-of-vocabulary words and better disambiguation, an improvement over an existing morphological analyser MorphoDiTa, at the same time, the deep learning model benefits from inference-time guidance of a manually curated morphological dictionary. We achieve 50% error reduction in lemmatization and 58% error reduction in POS tagging over MorphoDiTa, while also offering dependency parsing. The model is trained on one of the currently largest Czech morphosyntactic corpora, the PDT-C 1.0, with the trained models available at https://hdl.handle.net/11234/1-5293. We provide the tool as a web service deployed at https://lindat.mff.cuni.cz/services/udpipe/. The source code is available at GitHub (https://github.com/ufal/udpipe/tree/udpipe-2), along with a Python client for a simple use. The documentation for the models can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model. |
| title | Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.12422 |