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1
The ParlaMint corpora of parliamentary proceedings
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2
The ParlaMint corpora of parliamentary proceedings
In: Lang Resour Eval (2022)
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3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
Multilingual comparable corpora of parliamentary debates ParlaMint 2.1
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7
Linguistically annotated multilingual comparable corpora of parliamentary debates ParlaMint.ana 2.1
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8
Linguistically annotated multilingual comparable corpora of parliamentary debates ParlaMint.ana 2.0
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9
Multilingual comparable corpora of parliamentary debates ParlaMint 2.0
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10
Language Technology Programme for Icelandic 2019-2023 ...
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11
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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12
A Universal Dependencies Conversion Pipeline for a Penn-format Constituency Treebank ...
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13
Augmenting a BiLSTM tagger with a Morphological Lexicon and a Lexical Category Identification Step ...
Abstract: Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any previously published tagger not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform previous state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent in morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input for the main model. This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic. ... : Accepted by RANLP 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://arxiv.org/abs/1907.09038
https://dx.doi.org/10.48550/arxiv.1907.09038
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