2019
ACL
ACL 2019
TLR at BSNLP2019: A Multilingual Named Entity Recognition System
Abstract
AbstractThis paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60% to 83% in terms of Strict and Relaxed metrics, respectively.
🌉
Interdisciplinary Bridge
— Deep 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 > Named Entity Recognition
Natural Language Processing > Resources & Methods > Multilingual NLP
Natural Language Processing > Applications > Named Entity Recognition
Deep Learning > Learning Types > Transfer Learning
Deep Learning > Learning Types > Ensemble Learning