A Simple Joint Model for Improved Contextual Neural Lemmatization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427122" target="_blank" >RIV/00216208:11320/19:10427122 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/N19-1155" target="_blank" >https://www.aclweb.org/anthology/N19-1155</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A Simple Joint Model for Improved Contextual Neural Lemmatization
Original language description
English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known as the lemma. We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. Our paper describes the model in addition to training and decoding procedures. Error analysis indicates that joint morphological tagging and lemmatization is especially helpful in low-resource lemmatization and languages that display a larger degree of morphological complexity.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů