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1
Learning English with Peppa Pig ...
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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations ...
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3
Discrete representations in neural models of spoken language ...
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4
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling ...
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5
Analyzing analytical methods: The case of phonology in neural models of spoken language ...
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6
Learning to Understand Child-directed and Adult-directed Speech ...
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7
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning ...
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8
On the difficulty of a distributional semantics of spoken language
In: Proceedings of the Society for Computation in Linguistics (2019)
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9
Lessons learned in multilingual grounded language learning ...
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10
Revisiting the Hierarchical Multiscale LSTM ...
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11
On the difficulty of a distributional semantics of spoken language ...
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12
Encoding of phonology in a recurrent neural model of grounded speech ...
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13
Rnn Models For Representation Of Linguistic Form And Function In Recurrent Neural Networks ...
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14
Rnn Models For Representation Of Linguistic Form And Function In Recurrent Neural Networks ...
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15
Representations of language in a model of visually grounded speech signal ...
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16
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning ...
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17
Representation of linguistic form and function in recurrent neural networks ...
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18
Elephant: Sequence Labeling for Word and Sentence Segmentation
In: EMNLP 2013 ; https://hal.archives-ouvertes.fr/hal-01344500 ; EMNLP 2013, Oct 2013, Seattle, United States (2013)
Abstract: International audience ; Tokenization is widely regarded as a solved problem due to the high accuracy that rule-based tokenizers achieve. But rule-based tokenizers are hard to maintain and their rules language specific. We show that high-accuracy word and sentence segmentation can be achieved by using supervised sequence labeling on the character level combined with unsupervised feature learning. We evaluated our method on three languages and obtained error rates of 0.27 ‰ (English), 0.35 ‰ (Dutch) and 0.76 ‰ (Italian) for our best models .
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Deep learning; Machine learning; segmentation; Sequence labeling; Tokenization
URL: https://hal.archives-ouvertes.fr/hal-01344500/document
https://hal.archives-ouvertes.fr/hal-01344500
https://hal.archives-ouvertes.fr/hal-01344500/file/D13-1146.pdf
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19
Text segmentation with character-level text embeddings ...
Chrupała, Grzegorz. - : arXiv, 2013
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20
Elephant: Sequence labeling for word and sentence segmentation
Evang, Kilian; Basile, Valerio; Chrupała, Grzegorz. - : Association for Computational Linguistics (ACL), 2013. : country:USA, 2013. : place:Stroudsburg, 2013
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