Summary form only given. 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. This talk argues for the necessity of (even shallow) semantics-based NLP, describes the contents and operation of the OntoNotes project, and in so doing introduces and explains the general issues facing annotation projects. Our hope is that other people not only try to use the OntoNotes corpus in their own work, but also create their own annotations on the same material, so that more layers of shallow semantics can be included into OntoNotes.