tailieunhanh - Báo cáo khoa học: "A Study on Convolution Kernels for Shallow Semantic Parsing"

In this paper we have designed and experimented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process structured representations. Support Vector Machines (SVMs), using a combination of such kernels and the flat feature kernel, classify PropBank predicate arguments with accuracy higher than the current argument classification stateof-the-art. | 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 Abstract In this paper we have designed and experimented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process structured representations. Support Vector Machines SVMs using a combination of such kernels and the flat feature kernel classify PropBank 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 proposed kernels do not produce any improvement. 1 Introduction Several linguistic theories . Jackendoff 1990 claim that semantic information in natural language texts is connected to syntactic structures. Hence to deal with natural language 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 processing of predicate argument structures annotated 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 latter are schematic representations of situations involving .