In recent years tree kernels have been proposed for the automatic learning of natural language applications . Unfortunately , they show ( a ) an inherent super linear complexity and ( b ) a lower accuracy than traditional attribute / value methods . In this paper , we show that tree kernels are very helpful in the processing of natural language as ( a ) we provide a simple algorithm to compute tree kernels in linear average running time and ( b ) our study on the classification properties of diverse tree kernels show that kernel combinations always improve the traditional methods . Experiments with Support Vector Machines on the predicate argument classification task provide empirical support to our thesis . In recent years tree kernels have been shown to be interesting approaches for the modeling of syntactic information in natural language tasks , e.g. syntactic parsing ( Collins and Duffy , 2002 ) , relation extraction ( Zelenko et al . , 2003 ) , Named Entity recognition ( Cumby and Roth , 2003 ; Culotta and Sorensen , 2004 ) and Semantic Parsing ( Moschitti , 2004 ) . The main tree kernel advantage is the possibility to generate a high number of syntactic features and let the learning algorithm to select those most relevant for a specific application . In contrast , their major drawback are ( a ) the computational time complexity which is superlinear in the number of tree nodes and ( b ) the accuracy that they produce is often lower than the one provided by linear models on manually designed features . To solve problem ( a ) , a linear complexity algorithm for the subtree ( ST ) kernel computation , was designed in ( Vishwanathan and Smola , 2002 ) . Unfortunately , the ST set is rather poorer than the one generated by the subset tree ( SST ) kernel designed in ( Collins and Duffy , 2002 ) . Intuitively , an ST rooted in a node n of the target tree always contains all n 's descendants until the leaves . This does not hold for the SSTs whose leaves can be internal nodes . To solve the problem ( b ) , a study on different tree substructure spaces should be carried out to derive the tree kernel that provide the highest accuracy . On the one hand , SSTs provide learning algorithms with richer information which may be critical to capture syntactic properties of parse trees as shown , for example , in ( Zelenko et al . , 2003 ; Moschitti , 2004 ) . On the other hand , if the SST space contains too many irrelevant features , overfitting may occur and decrease the classification accuracy ( Cumby and Roth , 2003 ) . As a consequence , the fewer features of the ST approach may be more appropriate . In this paper , we aim to solve the above problems . We present ( a ) an algorithm for the evaluation of the ST and SST kernels which runs in linear average time and ( b ) a study of the impact of diverse tree kernels on the accuracy of Support Vector Machines ( SVMs ) . Our fast algorithm computes the kernels between two syntactic parse trees in O(m + n ) average time , where m and n are the number of nodes in the two trees . This low complexity allows SVMs to carry out experiments on hundreds of thousands of training instances since it is not higher than the complexity of the polynomial ker-nel , widely used on large experimentation e.g. ( Pradhan et al . , 2004 ) . To confirm such hypothesis , we measured the impact of the algorithm on the time required by SVMs for the learning of about 122,774 predicate argument examples annotated in PropBank ( Kingsbury and Palmer , 2002 ) and 37,948 instances annotated in FrameNet ( Fillmore , 1982 ) . Regarding the classification properties , we studied the argument labeling accuracy of ST and SST kernels and their combinations with the standard features ( Gildea and Jurafsky , 2002 ) . The results show that , on both PropBank and FrameNet datasets , the SST-based kernel , i.e. the richest in terms of substructures , produces the highest SVM accuracy . When SSTs are combined with the manual designed features , we always obtain the best figure classifier . This suggests that the many fragments included in the SST space are relevant and , since their manual design may be problematic ( requiring a higher programming effort and deeper knowledge of the linguistic phenomenon ) , tree kernels provide a remarkable help in feature engineering . In the remainder of this paper , Section 2 describes the parse tree kernels and our fast algorithm . Section 3 introduces the predicate argument classification problem and its solution . Section 4 shows the comparative performance in term of the execution time and accuracy . Finally , Section 5 discusses the related work whereas Section 6 summarizes the conclusions . In this paper , we have shown that tree kernels can effectively be adopted in practical natural language applications . The main arguments against their use are their efficiency and accuracy lower than traditional feature based approaches . We have shown that a fast algorithm ( FTK ) can evaluate tree kernels in a linear average running time and also that the overall converging time required by SVMs is compatible with very large data sets . Regarding the accuracy , the experiments with Support Vector Machines on the PropBank and FrameNet predicate argument structures show that : ( a ) the richer the kernel is in term of substructures ( e.g. SST ) , the higher the accuracy is , ( b ) tree kernels are effective also in case of automatic parse trees and ( c ) as kernel combinations always improve traditional feature models , the best approach is to combine scalar-based and structured based kernels .

Challenge: While tree kernels enjoy generating a high number of syntactic features and use them flexibly, they suffer from super linear complexity and lower accuracies.
Approach: They propose an algorithm which runs in linear average time to evaluate tree kernels used in support vector machines.
Outcome: Their analysis with the proposed evaluation method shows that a fast algorithm can run in linear time and richer kernels achieve higher accuracy.