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.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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 Paradigms > Transfer Learning
Machine Learning > Learning Paradigms > Multi-Task Learning
Deep Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Text Processing