Optimal Constituent Alignment with Edge Covers for Semantic Projection

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 1161–1168, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Optimal Constituent Alignment with Edge Covers for Semantic Projection Sebastian Padó Computational Linguistics Saarland University Saarbrücken, Germany pado@coli.uni-sb.de Mirella Lapata School of Informatics University of Edinburgh Edinburgh, UK mlap@inf.ed.ac.uk Abstract Given a parallel corpus, semantic projec- tion attempts to transfer semantic role an- notations from one language to another, typically by exploiting word alignments. In this paper, we present an improved method for obtaining constituent align- ments between parallel sentences to guide the role projection task. Our extensions are twofold: (a) we model constituent alignment as minimum weight edge cov- ers in a bipartite graph, which allows us to find a globally optimal solution efficiently; (b) we propose tree pruning as a promising strategy for reducing alignment noise. Ex- perimental results on an English-German parallel corpus demonstrate improvements over state-of-the-art models. 1 Introduction Recent years have witnessed increased interest in data-driven methods for many natural language processing (NLP) tasks, ranging from part-of- speech tagging, to parsing, and semantic role la- belling. The success of these methods is due partly to the availability of large amounts of training data annotated with rich linguistic information. Unfor- tunately, such resources are largely absent for al- most all languages except English. Given the data requirements for supervised learning, and the cur- rent paucity of suitable data for many languages, methods for generating annotations (semi-)auto- matically are becoming increasingly popular. Annotation projection tackles this problem by leveraging parallel corpora and the high-accuracy tools (e.g., parsers, taggers) available for a few languages. Specifically, through the use of word alignments, annotations are transfered from resource-rich languages onto low density ones. The projection process can be decomposed into three steps: (a) determining the units of projection; these are typically words but can also be chunks or syntactic constituents; (b) inducing alignments between the projection units and projecting anno- tations along these alignments; (c) reducing the amount of noise in the projected annotations, often due to errors and omissions in the word alignment. The degree to which analyses are parallel across languages is crucial for the success of projection approaches. A number of recent studies rely on this notion of parallelism and demonstrate that an- notations can be adequately projected for parts of speech (Yarowsky and Ngai, 2001; Hi and Hwa, 2005), chunks (Yarowsky and Ngai, 2001), and de- pendencies (Hwa et al., 2002). In previous work (Padó and Lapata, 2005) we considered the annotation projection of seman- tic roles conveyed by sentential constituents such as AGENT, PATIENT, or INSTRUMENT. Semantic roles exhibit a high degree of parallelism across languages (Boas, 2005) and thus appear amenable to projection. Furthermore, corpora labelled with semantic role information can be used to train shallow semantic parsers (Gildea and Jurafsky, 2002), which could in turn benefit applications in need of broad-coverage semantic analysis. Exam- ples include question answering, information ex- traction, and notably machine translation. Our experiments …

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