A Study on Convolution Kernels for Shallow Statistic Parsing

A Study on Convolution Kernels for Shallow Semantic Parsing Alessandro Moschitti University of Texas at Dallas Human Language Technology Research Institute Richardson, TX 75083-0688, USA alessandro.moschitti@utdallas.edu Abstract In this paper we have designed and experi- mented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process struc- tured representations. Support Vector Ma- chines (SVMs), using a combination of such ker- nels and the flat feature kernel, classify Prop- Bank predicate arguments with accuracy higher than the current argument classification state- of-the-art. Additionally, experiments on FrameNet data have shown that SVMs are appealing for the classification of semantic roles even if the pro- posed kernels do not produce any improvement. 1 Introduction Several linguistic theories, e.g. (Jackendoff, 1990) claim that semantic information in nat- ural language texts is connected to syntactic structures. Hence, to deal with natural lan- guage semantics, the learning algorithm should be able to represent and process structured data. The classical solution adopted for such tasks is to convert syntax structures into flat feature representations which are suitable for a given learning model. The main drawback is that structures may not be properly represented by flat features. In particular, these problems affect the pro- cessing of predicate argument structures an- notated in PropBank (Kingsbury and Palmer, 2002) or FrameNet (Fillmore, 1982). Figure 1 shows an example of a predicate annotation in PropBank for the sentence: "Paul gives a lecture in Rome". A predicate may be a verb or a noun or an adjective and most of the time Arg 0 is the logical subject, Arg 1 is the logical object and ArgM may indicate locations, as in our example. FrameNet also describes predicate/argument structures but for this purpose it uses richer semantic structures called frames. These lat- ter are schematic representations of situations involving various participants, properties and roles in which a word may be typically used. Frame elements or semantic roles are arguments of predicates called target words. In FrameNet, the argument names are local to a particular frame. Predicate Arg. 0 Arg. M S N NP D N VP V Paul in gives a lecture PP IN N Rome Arg. 1 Figure 1: A predicate argument structure in a parse-tree representation. Several machine learning approaches for argu- ment identification and classification have been developed (Gildea and Jurasfky, 2002; Gildea and Palmer, 2002; Surdeanu et al., 2003; Ha- cioglu et al., 2003). Their common characteris- tic is the adoption of feature spaces that model predicate-argument structures in a flat repre- sentation. On the contrary, convolution kernels aim to capture structural information in term of sub-structures, providing a viable alternative to flat features. In this paper, we select portions of syntactic trees, which include predicate/argument salient sub-structures, to define convolution kernels for the task of predicate argument classification. In particular, our kernels aim to (a) represent the relation between predicate and one of its argu- ments and (b) to capture the overall argument structure of the target predicate. Additionally, we define …

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