Abstract 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 …

In this paper we describe the CoNLL-2005 shared task on Semantic Role Labeling. We
introduce the specification and goals of the task, describe the data sets and evaluation …

Categorial Grammar (CG; Ajdukiewicz 1935; Bar-Hillel 1953) is one of the oldest lexicalized
grammar formalisms, in which all grammatical constituents are distinguished by a syntactic …

We present a general framework for semantic role labeling. The framework combines a
machine-learning technique with an integer linear programming-based inference procedure …

In this paper, we propose a machine learning algorithm for shallow semantic parsing,
extending the work of Gildea and Jurafsky (2002), Surdeanu et al.(2003) and others. Our …

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The natural language processing community has recently experienced a growth of interest
in domain independent shallow semantic parsing—the process of assigning a Who did What …

We present a system for the semantic role labeling task. The system combines a machine
learning technique with an inference procedure based on integer linear programming that …

Semantic role labeling is the process of annotating the predicate-argument structure in text
with semantic labels. In this paper we present a state-of-the-art baseline semantic role …

This dissertation is concerned with the creation of training data and the development of
probability models for statistical parsing of English with Combinatory Categorial Grammar …