Much of NLP tries to map structured input (sentences) to some form of structured output (tag sequences, parse trees, semantic graphs, or translated/paraphrased/compressed sentences). Thus structured prediction and its learning algorithm are of central importance to us NLP researchers. However, when applying machine learning to structured domains, we often face scalability issues for two reasons:
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Scalable Large-Margin Structured Learning: Theory and Algorithms
Semantic Scholar · Computer Science · 2014
Abstract
Much of NLP tries to map structured input (sentences) to some form of structured output (tag sequences, parse trees, semantic graphs, or translated/paraphrased/compressed sentences). Thus structured prediction and its learning algorithm are of central importance to us NLP researchers. However, when applying machine learning to structured domains, we often face scalability issues for two reasons: