A Relation-Centric View of Semantic Representation Learning

Much of NLP can be described as mapping of a message from one sequence of symbols to another. Examples include word surface forms, POS-tags, parse trees, vocabularies of different languages, etc. Machine learning has been applied successfully to many NLP problems by adopting the symbol mapping view. No knowledge of the sets of symbols is required: only a rich feature representation to map between them.