Semantic parsing based on FrameNet

Semantic Parsing Based on FrameNet Cosmin Adrian Bejan, Alessandro Moschitti, Paul Mor˘arescu, Gabriel Nicolae and Sanda Harabagiu University of Texas at Dallas Human Language Technology Research Institute Richardson, TX 75083-0688, USA Abstract This paper describes our method based on Support Vector Machines for automatically assigning seman- tic roles to constituents of English sentences. This method employs four different feature sets, one of which being first reported herein. The combination of features as well as the extended training data we considered have produced in the Senseval-3 experi- ments an F1-score of 92.5% for the unrestricted case and of 76.3% for the restricted case. 1 Introduction The evaluation of the Senseval-3 task for Automatic Labeling of Semantic Roles is based on the annota- tions made available by the FrameNet Project (Baker et al., 1998). The idea of automatically identifying and labeling frame-specific roles, as defined by the semantic frames, was first introduced by (Gildea and Jurasfky, 2002). Each semantic frame is character- ized by a set of target words which can be nouns, verbs or adjectives. This helps abstracting the the- matic roles and adding semantics to the given frame, highlighting the characteristic semantic features. Frames are characterized by (1) target words or lexical predicates whose meaning includes aspects of the frame; (2) frame elements (FEs) which represent the semantic roles of the frame and (3) examples of annotations performed on the British National Cor- pus (BNC) for instances of each target word. Thus FrameNet frames are schematic representations of situations lexicalized by the target words (predicates) in which various participants and conceptual roles are related (the frame elements), exemplified by sen- tences from the BNC in which the target words and the frame elements are annotated. In Senseval-3 two different cases of automatic la- beling of the semantic roles were considered. The Unrestricted Case requires systems to assign FE la- bels to the test sentences for which (a) the bound- aries of each frame element were given and the tar- get words identified. The Restricted Case requires systems to (i) recognize the boundaries of the FEs for each evaluated frame as well as to (ii) assign a label to it. Both cases can be cast as two different classifications: (1) a classification of the role when its boundaries are known and (2) a classification of the sentence words as either belonging to a role or not1. A similar approach was used for automati- cally identifying predicate-argument structures in English sentences. The PropBank annotations (www.cis.upenn.edu/∼ace) enable training for two distinct learning techniques: (1) decision trees (Sur- deanu et al., 2003) and (2) Support Vector Machines (SVMs) (Pradhan et al., 2004). The SVMs produced the best results, therefore we decided to use the same learning framework for the Senseval-3 task for Auto- matic Labeling of Semantic Roles. Additionally, we have performed the following enhancements: • we created a multi-class classifier for each frame, thus achieving improved accuracy and efficiency; • we combined some new features with features from (Gildea and Jurasfky, 2002; Surdeanu …

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