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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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2
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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3
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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4
Formalization of AMR Inference via Hybrid Logic Tableaux ...
Goldner, Eli Tecumseh. - : Brandeis University, 2022
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5
Chinese character decomposition for neural MT with multi-word expressions
In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 , Smeaton, Alan F. orcid:0000-0003-1028-8389 and Bolzoni, Paolo (2021) Chinese character decomposition for neural MT with multi-word expressions. In: 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), 31 May- 2 June 2021, Reykjavik, Iceland (Online). (In Press) (2021)
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6
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing ...
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7
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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8
10D: Phonology, Morphology and Word Segmentation #1 ...
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9
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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10
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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11
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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12
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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13
The Match-Extend Serialization Algorithm in Multiprecedence ...
Abstract: Raimy (1999; 2000a; 2000b) proposed a graphical formalism for modeling reduplication, originallymostly focused on phonological overapplication in a derivational framework. This framework is now known as Precedence-based phonology or Multiprecedence phonology. Raimy's idea is that the segments at the input to the phonology are not totally ordered by precedence. This paper tackles a challenge that arose with Raimy's work, the development of a deterministic serialization algorithm as part of the derivation of surface forms. The Match-Extend algorithm introduced here requires fewer assumptions and sticks tighter to the attested typology. The algorithm also contains no parameter or constraint specific to individual graphs or topologies, unlike previous proposals. Match-Extend requires nothing except knowing the last added set of links. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/30079-the-match-extend-serialization-algorithm-in-multiprecedence
https://dx.doi.org/10.48448/rgky-kt38
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14
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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15
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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16
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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17
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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18
Including Signed Languages in Natural Language Processing ...
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19
Quantification: the view from natural language generation ...
Carstensen, Kai-Uwe. - : Universitätsbibliothek Siegen, 2021
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20
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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