2018
EMNLP
EMNLP 2018
Interpretable Word Embedding Contextualization
Abstract
AbstractIn this paper, we propose a method of calibrating a word embedding, so that the semantic it conveys becomes more relevant to the context. Our method is novel because the output shows clearly which senses that were originally presented in a target word embedding become stronger or weaker. This is possible by utilizing the technique of using sparse coding to recover senses that comprises a word embedding.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🐣
Hot Topic Early Bird
— word sense disambiguation
🐝
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
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Optimization & Theory > Theory
Natural Language Processing > Resources & Methods > Text Representation
Deep Learning > Techniques > Representation Learning
Machine Learning > Core Methods > Sparse Coding