Unsupervised Argument Identification for Semantic Role Labeling

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 28–36, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Unsupervised Argument Identification for Semantic Role Labeling Omri Abend1 Roi Reichart2 Ari Rappoport1 1Institute of Computer Science , 2ICNC Hebrew University of Jerusalem {omria01|roiri|arir}@cs.huji.ac.il Abstract 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 corpus. This is the first paper that tackles unsupervised verb argument identification without using manually encoded rules or extensive lexi- cal or syntactic resources. 1 Introduction Semantic Role Labeling (SRL) is a major NLP task, providing a shallow sentence-level semantic analysis. SRL aims at identifying the relations be- tween the predicates (usually, verbs) in the sen- tence and their associated arguments. The SRL task is often viewed as consisting of two parts: argument identification (ARGID) and ar- gument classification. The former aims at identi- fying the arguments of a given predicate present in the sentence, while the latter determines the type of relation that holds between the identi- fied arguments and their corresponding predicates. The division into two sub-tasks is justified by the fact that they are best addressed using differ- ent feature sets (Pradhan et al., 2005). Perfor- mance in the ARGID stage is a serious bottleneck for general SRL performance, since only about 81% of the arguments are identified, while about 95% of the identified arguments are labeled cor- rectly (M`arquez et al., 2008). SRL is a complex task, which is reflected by the algorithms used to address it. A standard SRL al- gorithm requires thousands to dozens of thousands sentences annotated with POS tags, syntactic an- notation and SRL annotation. Current algorithms show impressive results but only for languages and domains where plenty of annotated data is avail- able, e.g., English newspaper texts (see Section 2). Results are markedly lower when testing is on a domain wider than the training one, even in En- glish (see the WSJ-Brown results in (Pradhan et al., 2008)). Only a small number of works that do not re- quire manually labeled SRL training data have been done (Swier and Stevenson, 2004; Swier and Stevenson, 2005; Grenager and Manning, 2006). These papers have replaced this data with the VerbNet (Kipper et al., 2000) lexical resource or a set of manually written rules and supervised …

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