The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ‘‘trace’ ’ categories of the treebank.

We describe a parallel annotation approach for PubMed abstracts. It includes both entity/relation annotation and a treebank containing syntactic structure, with a goal of mapping entities to constituents in the treebank. Crucial to this approach is a modification of the Penn Treebank guidelines and the characterization of entities as relation components, which allows the integration of the entity annotation with the syntactic structure while retaining the capacity to annotate and extract more complex events.

This paper compares the performance of a Tree-to-string (TTS) transducer with automatically generated/gold-standard parse trees and semantic roles. Experimental results show that improving the parsing quality can lead to significant improvement in MT performance and adding semantic roles in the syntax tree labels does not improve the TTS transducer. Another approach of using semantic roles: skeleton template extraction, is proposed and shown to be better than extracting straight long templates down to a certain depth. 1