← Back to papers
2017 CONLL CoNLL 2017

Neural Structural Correspondence Learning for Domain Adaptation

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
📈 Trend Setter — Autoencoders
🧭 Keyword Pioneer — structural correspondence learning
🐣 Hot Topic Early Bird — representation learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Yftah Ziser , Roi Reichart

Topics

Machine Learning > Application Areas > Domain Adaptation Deep Learning > Architectures > Autoencoders Natural Language Processing Machine Learning > Learning Types > Transfer Learning Machine Learning > Learning Types > Domain Adaptation

Keywords

representation learning domain adaptation word embedding structural correspondence learning autoencoder neural network cross-domain sentiment classification pivot feature
Download PDF

Related papers

Neural Sequence-to-sequence Learning of Internal Word Structure 2017
An Automatic Approach for Document-level Topic Model Evaluation 2017
Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts 2017
Knowledge Tracing in Sequential Learning of Inflected Vocabulary 2017
A Probabilistic Generative Grammar for Semantic Parsing 2017