Sequence-to-sequence Tagging
Sequence-to-Sequence Tagging is a sequence labeling framework that maps an input sequence to an output sequence of structured labels through an encoder-decoder architecture, distinguishing itself from traditional tagging models by enabling flexible, end-to-end learning of sequential dependencies between variable-length input and output sequences.
159
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556
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193
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2017–2026
108
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Discriminative training methods for hidden Markov models
Michael Collins · 2002 · 1.9K citations · Full text
Text Chunking using Transformation-Based Learning
Lance Ramshaw, Mitchell P. Marcus · ArXiv.org · 1995 · 474 citations · Full text
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang et al. · 2023 · 457 citations · Full text
Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Lu Xu, Hao Li, Wei Lu et al. · 2020 · 265 citations · Full text
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Google (United States)
Mountain View, United States
6 h-index
36 publications
2.1K citations
Singapore University of Technology and Design
Singapore, Singapore
4 h-index
7 publications
959 citations
University of Cambridge
Cambridge, United Kingdom
4 h-index
7 publications
536 citations
Stanford University
Stanford, United States
4 h-index
11 publications
1.5K citations

University of Groningen
Groningen, The Netherlands
3 h-index
8 publications
418 citations

Amrita Vishwa Vidyapeetham
Coimbatore, India
3 h-index
8 publications
208 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
9 publications
2.2K citations
Universidad del Noreste
Tampico, Mexico
3 h-index
5 publications
504 citations
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Repository · 31 publications · 1.6K citations
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TASLP · 5 publications · 180 citations
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Repository · 2 publications · 29 citations
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