2018
EMNLP
EMNLP 2018
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging
Abstract
Abstracta cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
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Hot Topic Early Bird
- low-resource language
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Understanding > Part-of-Speech Tagging
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Transfer Learning
Deep Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Text Processing