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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
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing ...
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5
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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6
10D: Phonology, Morphology and Word Segmentation #1 ...
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7
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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8
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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9
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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10
The Match-Extend Serialization Algorithm in Multiprecedence ...
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11
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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12
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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13
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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14
Including Signed Languages in Natural Language Processing ...
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15
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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16
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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17
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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18
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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19
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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20
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.347 Abstract: With the increasing interest in low-resource languages, unsupervised morphological segmentation has become an active area of research, where approaches based on Adaptor Grammars achieve state-of-the-art results. We demonstrate the power of harnessing linguistic knowledge as priors within Adaptor Grammars in a minimally-supervised learning fashion. We introduce two types of priors: 1) grammar definition, where we design language-specific grammars; and 2) linguist-provided affixes, collected by an expert in the language and seeded into the grammars. We use Japanese and Georgian as respective case studies for the two types of priors and introduce new datasets for these languages, with gold morphological segmentation for evaluation. We show that the use of priors results in error reductions of 8.9% and 34.2%, respectively, over the equivalent state-of-the-art unsupervised system. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Morphology; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/j68h-8888
https://underline.io/lecture/26438-minimally-supervised-morphological-segmentation-using-adaptor-grammars-with-linguistic-priors
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