Towards Shallow Semantics: The OntoNotes Project

摘要

Many natural language processing (NLP) applications could benefit from a richer model of text meaning than the bag-of-words and n-gram models that currently predominate. Despite theoretical interest since the 1960s, however, no large-scale model exists; in fact, it is not even clear what such a model should minimally include. However, the introduction of large-scale public resources such as the Penn TreeBank and WordNet have generated a great deal of progress in the NLP community, and so it seems increasingly important to create some kind of meaning-oriented model and build a corresponding corpus that is large enough to support adequate machine learning.