Cross-linguistic Projection of Role-Semantic Information

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 859–866, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Cross-linguistic Projection of Role-Semantic Information 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 This paper considers the problem of auto- matically inducing role-semantic annota- tions in the FrameNet paradigm for new languages. We introduce a general frame- work for semantic projection which ex- ploits parallel texts, is relatively inexpen- sive and can potentially reduce the amount of effort involved in creating semantic re- sources. We propose projection models that exploit lexical and syntactic informa- tion. Experimental results on an English- German parallel corpus demonstrate the advantages of this approach. 1 Introduction Shallow semantic parsing, the task of automatically identifying the semantic roles conveyed by senten- tial constituents, has recently attracted much atten- tion, partly because of its increasing importance for potential applications. For instance, information ex- traction (Surdeanu et al., 2003), question answer- ing (Narayanan and Harabagiu, 2004) and machine translation (Boas, 2002) could stand to benefit from broad coverage semantic processing. The FrameNet project (Fillmore et al., 2003) has played a central role in this endeavour by providing a large lexical resource based on se- mantic roles. In FrameNet, meaning is represented by frames, schematic representations of situations. Semantic roles are frame-specific, and are called frame elements. The database associates frames with lemmas (verbs, nouns, adjectives) that can evoke them (called frame-evoking elements or FEEs), lists the possible syntactic realisations of their seman- tic roles, and provides annotated examples from the British National Corpus (Burnard, 1995). The avail- ability of rich annotations for the surface realisation of semantic roles has triggered interest in semantic parsing and enabled the development of data-driven models (e.g., Gildea and Jurafsky, 2002). Frame: DEPARTING THEME The officer left the house. The plane leaves at seven. His departure was delayed. SOURCE We departed from New York. He retreated from his opponent. The woman left the house. Frame Elements FEEs abandon.v, desert.v, depart.v, departure.n, emerge.v, emigrate.v, emigration.n, escape.v, escape.n, leave.v, quit.v, retreat.v, retreat.n, split.v, withdraw.v, withdrawal.n Table 1: Example of FrameNet frame Table 1 illustrates an example from the FrameNet database, the DEPARTING frame. It has two roles, a THEME which is the moving object and a SOURCE expressing the initial position of the THEME. The frame elements are realised by different syntactic ex- pressions. For instance, the THEME is typically an NP, whereas the SOURCE is often expressed by a prepositional phrase (see the expressions in boldface in Table 1). The DEPARTING frame can be evoked by abandon, desert, depart, and several other verbs as well as nouns (see the list of FEEs in Table 1). Although recent advances in semantic parsing1 have greatly benefited from the availability of the English FrameNet, unfortunately such resources are largely absent for other languages. The English FrameNet (Version 1.1) contains 513 frames cov- ering 7,125 lexical items and has …

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