Convolution tree kernel has shown promising results in semantic role classification . However , it only carries out hard matching , which may lead to over-fitting and less accurate similarity measure . To remove the constraint , this paper proposes a grammardriven convolution tree kernel for semantic role classification by introducing more linguistic knowledge into the standard tree kernel . The proposed grammar-driven tree kernel displays two advantages over the previous one : 1 ) grammar-driven approximate substructure matching and 2 ) grammardriven approximate tree node matching . The two improvements enable the grammardriven tree kernel explore more linguistically motivated structure features than the previous one . Experiments on the CoNLL-2005 SRL shared task show that the grammardriven tree kernel significantly outperforms the previous non-grammar-driven one in SRL . Moreover , we present a composite kernel to integrate feature-based and tree kernel-based methods . Experimental results show that the composite kernel outperforms the previously best-reported methods . Given a sentence , the task of Semantic Role Labeling ( SRL ) consists of analyzing the logical forms expressed by some target verbs or nouns and some constituents of the sentence . In particular , for each predicate ( target verb or noun ) all the constituents in the sentence which fill semantic arguments ( roles ) of the predicate have to be recognized . Typical semantic roles include Agent , Patient , Instrument , etc . and also adjuncts such as Locative , Temporal , Manner , and Cause , etc . Generally , semantic role identification and classification are regarded as two key steps in semantic role labeling . Semantic role identification involves classifying each syntactic element in a sentence into either a semantic argument or a non-argument while semantic role classification involves classifying each semantic argument identified into a specific semantic role . This paper focuses on semantic role classification task with the assumption that the semantic arguments have been identified correctly . Both feature-based and kernel-based learning methods have been studied for semantic role classification ( Carreras and Màrquez , 2004 ; Carreras and Màrquez , 2005 ) . In feature-based methods , a flat feature vector is used to represent a predicateargument structure while , in kernel-based methods , a kernel function is used to measure directly the similarity between two predicate-argument structures . As we know , kernel methods are more effective in capturing structured features . Moschitti ( 2004 ) and Che et al . ( 2006 ) used a convolution tree kernel ( Collins and Duffy , 2001 ) for semantic role classification . The convolution tree kernel takes sub-tree as its feature and counts the number of common sub-trees as the similarity between two predicate-arguments . This kernel has shown very promising results in SRL . However , as a general learning algorithm , the tree kernel only carries out hard matching between any two sub-trees without considering any linguistic knowledge in kernel design . This makes the kernel fail to handle similar phrase structures ( e.g. , " buy a car " vs. " buy a red car " ) and near-synonymic grammar tags ( e.g. , the POS variations between " high / JJ degree / NN " 1 and " higher / JJR degree / NN " ) 2 . To some degree , it may lead to over-fitting and compromise performance . This paper reports our preliminary study in addressing the above issue by introducing more linguistic knowledge into the convolution tree kernel . To our knowledge , this is the first attempt in this research direction . In detail , we propose a grammar-driven convolution tree kernel for semantic role classification that can carry out more linguistically motivated substructure matching . Experimental results show that the proposed method significantly outperforms the standard convolution tree kernel on the data set of the CoNLL-2005 SRL shared task . The remainder of the paper is organized as follows : Section 2 reviews the previous work and Section 3 discusses our grammar-driven convolution tree kernel . Section 4 shows the experimental results . We conclude our work in Section 5 . In this paper , we propose a novel grammar-driven convolution tree kernel for semantic role classification . More linguistic knowledge is considered in the new kernel design . The experimental results verify that the grammar-driven kernel is more effective in capturing syntactic structure features than the previous convolution tree kernel because it allows grammar-driven approximate matching of substructures and node features . We also discuss the criteria to determine the optional nodes in a CFG rule in defining our grammar-driven convolution tree kernel . The extension of our work is to improve the performance of the entire semantic role labeling system using the grammar-driven tree kernel , including all four stages : pruning , semantic role identification , classification and post inference . In addition , a more interesting research topic is to study how to integrate linguistic knowledge and tree kernel methods to do feature selection for tree kernelbased NLP applications ( Suzuki et al . , 2004 ) . In detail , a linguistics and statistics-based theory that can suggest the effectiveness of different substructure features and whether they should be generated or not by the tree kernels would be worked out .
Despite its success in semantic role classification, convolution tree kernels based on the hard matching between two sub-trees suffer from over-fitting.
They propose to integrate a linguistically motivated grammar-baed convolution tree kernel into a standard tree kernel to achieve better substructure matching and tree node matching.
The new grammar-driven tree kernel significantly outperforms baseline kernels on the CoNLL-2005 task.