Unsupervised Semantic Role Induction via Split-Merge Clustering

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1117–1126, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Unsupervised Semantic Role Induction via Split-Merge Clustering Joel Lang and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB, UK J.Lang-3@sms.ed.ac.uk, mlap@inf.ed.ac.uk Abstract In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acqui- sition bottleneck associated with supervised role labelers. We present an algorithm that it- eratively splits and merges clusters represent- ing semantic roles, thereby leading from an initial clustering to a final clustering of bet- ter quality. The method is simple, surpris- ingly effective, and allows to integrate lin- guistic knowledge transparently. By com- bining role induction with a rule-based com- ponent for argument identification we obtain an unsupervised end-to-end semantic role la- beling system. Evaluation on the CoNLL 2008 benchmark dataset demonstrates that our method outperforms competitive unsuper- vised approaches by a wide margin. 1 Introduction Recent years have seen increased interest in the shal- low semantic analysis of natural language text. The term is most commonly used to describe the au- tomatic identification and labeling of the seman- tic roles conveyed by sentential constituents (Gildea and Jurafsky, 2002). Semantic roles describe the re- lations that hold between a predicate and its argu- ments, abstracting over surface syntactic configura- tions. In the example sentences below. window oc- cupies different syntactic positions — it is the object of broke in sentences (1a,b), and the subject in (1c) — while bearing the same semantic role, i.e., the physical object affected by the breaking event. Anal- ogously, rock is the instrument of break both when realized as a prepositional phrase in (1a) and as a subject in (1b). (1) a. [Joe]A0 broke the [window]A1 with a [rock]A2. b. The [rock]A2 broke the [window]A1. c. The [window]A1 broke. The semantic roles in the examples are labeled in the style of PropBank (Palmer et al., 2005), a broad-coverage human-annotated corpus of seman- tic roles and their syntactic realizations. Under the PropBank annotation framework (which we will as- sume throughout this paper) each predicate is as- sociated with a set of core roles (named A0, A1, A2, and so on) whose interpretations are specific to that predicate1 and a set of adjunct roles (e.g., loca- tion or time) whose interpretation is common across predicates. This type of semantic analysis is admit- tedly shallow but relatively straightforward to auto- mate and useful for the development of broad cov- erage, domain-independent language understanding systems. Indeed, the analysis produced by existing semantic role labelers has been shown to benefit a wide spectrum of applications ranging from infor- mation extraction (Surdeanu et al., 2003) and ques- tion answering (Shen and Lapata, 2007), to machine translation (Wu and Fung, 2009) and summarization (Melli et al., 2005). Since both argument identification and labeling can be readily modeled as classification tasks, most state-of-the-art systems to …

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