Semi-Supervised Frame-Semantic Parsing for Unknown Predicates

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1435–1444, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Semi-Supervised Frame-Semantic Parsing for Unknown Predicates Dipanjan Das and Noah A. Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {dipanjan,nasmith}@cs.cmu.edu Abstract We describe a new approach to disambiguat- ing semantic frames evoked by lexical predi- cates previously unseen in a lexicon or anno- tated data. Our approach makes use of large amounts of unlabeled data in a graph-based semi-supervised learning framework. We con- struct a large graph where vertices correspond to potential predicates and use label propa- gation to learn possible semantic frames for new ones. The label-propagated graph is used within a frame-semantic parser and, for un- known predicates, results in over 15% abso- lute improvement in frame identification ac- curacy and over 13% absolute improvement in full frame-semantic parsing F1 score on a blind test set, over a state-of-the-art supervised baseline. 1 Introduction Frame-semantic parsing aims to extract a shallow se- mantic structure from text, as shown in Figure 1. The FrameNet lexicon (Fillmore et al., 2003) is a rich linguistic resource containing expert knowl- edge about lexical and predicate-argument seman- tics. The lexicon suggests an analysis based on the theory of frame semantics (Fillmore, 1982). Recent approaches to frame-semantic parsing have broadly focused on the use of two statistical classifiers cor- responding to the aforementioned subtasks: the first one to identify the most suitable semantic frame for a marked lexical predicate (target, henceforth) in a sentence, and the second for performing semantic role labeling (SRL) given the frame. The FrameNet lexicon, its exemplar sentences containing instantiations of semantic frames, and full-text annotations provide supervision for learn- ing frame-semantic parsers. Yet these annotations lack coverage, including only 9,300 annotated tar- get types. Recent papers have tried to address the coverage problem. Johansson and Nugues (2007) used WordNet (Fellbaum, 1998) to expand the list of targets that can evoke frames and trained classifiers to identify the best-suited frame for the newly cre- ated targets. In past work, we described an approach where latent variables were used in a probabilistic model to predict frames for unseen targets (Das et al., 2010a).1 Relatedly, for the argument identifica- tion subtask, Matsubayashi et al. (2009) proposed a technique for generalization of semantic roles to overcome data sparseness. Unseen targets continue to present a major obstacle to domain-general se- mantic analysis. In this paper, we address the problem of idenfi- fying the semantic frames for targets unseen either in FrameNet (including the exemplar sentences) or the collection of full-text annotations released along with the lexicon. Using a standard model for the ar- gument identification stage (Das et al., 2010a), our proposed method improves overall frame-semantic parsing, especially for unseen targets. To better han- dle these unseen targets, we adopt a graph-based semi-supervised learning stategy (§4). We construct a large graph over potential targets, most of which 1Notwithstanding state-of-the-art results, that approach was only able to identify the …

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