The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argu- ment classification. Current SRL algo- rithms show lower results on the identifi- cation sub-task. Moreover, most SRL al- gorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised al- gorithm for identifying verb arguments, where the only type of annotation required is POS tagging. The algorithm makes use of a fully unsupervised syntactic parser, using its output in order to detect clauses and gather candidate argument colloca- tion statistics. We evaluate our algorithm on PropBank10, achieving a precision of 56%, as opposed to 47% of a strong base- line. We also obtain an 8% increase in precision for a Spanish corp...

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