2018
EMNLP
EMNLP 2018
Investigating NP-Chunking with Universal Dependencies for English
Abstract
AbstractChunking is a pre-processing task generally dedicated to improving constituency parsing. In this paper, we want to show that universal dependency (UD) parsing can also leverage the information provided by the task of chunking even though annotated chunks are not provided with universal dependency trees. In particular, we introduce the possibility of deducing noun-phrase (NP) chunks from universal dependencies, focusing on English as a first example. We then demonstrate how the task of NP-chunking can benefit PoS-tagging in a multi-task learning setting – comparing two different strategies – and how it can be used as a feature for dependency parsing in order to learn enriched models.
🌉
Interdisciplinary Bridge
— 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
Natural Language Processing > Understanding > Part-of-Speech Tagging
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Applications > Named Entity Recognition
Machine Learning > Learning Paradigms > Multi-Task Learning
Natural Language Processing > Applications > Natural Language Understanding