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Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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2
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers ...
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5
Deep learning for sentence clustering in essay grading support ...
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6
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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7
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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8
Towards Fully Bilingual Deep Language Modeling ...
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9
The birth of Romanian BERT ...
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10
WikiBERT models: deep transfer learning for many languages ...
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11
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT ...
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12
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
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13
Dependency parsing of biomedical text with BERT
In: BMC Bioinformatics (2020)
Abstract: BACKGROUND: : Syntactic analysis, or parsing, is a key task in natural language processing and a required component for many text mining approaches. In recent years, Universal Dependencies (UD) has emerged as the leading formalism for dependency parsing. While a number of recent tasks centering on UD have substantially advanced the state of the art in multilingual parsing, there has been only little study of parsing texts from specialized domains such as biomedicine. METHODS: : We explore the application of state-of-the-art neural dependency parsing methods to biomedical text using the recently introduced CRAFT-SA shared task dataset. The CRAFT-SA task broadly follows the UD representation and recent UD task conventions, allowing us to fine-tune the UD-compatible Turku Neural Parser and UDify neural parsers to the task. We further evaluate the effect of transfer learning using a broad selection of BERT models, including several models pre-trained specifically for biomedical text processing. RESULTS: : We find that recently introduced neural parsing technology is capable of generating highly accurate analyses of biomedical text, substantially improving on the best performance reported in the original CRAFT-SA shared task. We also find that initialization using a deep transfer learning model pre-trained on in-domain texts is key to maximizing the performance of the parsing methods.
Keyword: Research
URL: http://www.ncbi.nlm.nih.gov/pubmed/33372589
https://doi.org/10.1186/s12859-020-03905-8
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771067/
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14
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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15
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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16
Multilingual is not enough: BERT for Finnish ...
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17
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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18
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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19
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019
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20
A Neural Classification Method for Supporting the Creation of BioVerbNet ...
Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019
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