The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.
- Pub Date:
- December 2017
- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Computation and Language;
- Computer Science - Machine Learning;
- Computer Science - Logic in Computer Science
- PhD Thesis, University College London, Submitted and accepted in 2017