Semantic Role Recognition Using Kernels on Weighted Marked Ordered Labeled Trees

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 53–60, New York City, June 2006. c⃝2006 Association for Computational Linguistics Semantic Role Recognition using Kernels on Weighted Marked Ordered Labeled Trees Jun’ichi Kazama and Kentaro Torisawa Japan Advanced Institute of Science and Technology (JAIST) Asahidai 1-1, Nomi, Ishikawa, 923-1292 Japan {kazama, torisawa}@jaist.ac.jp Abstract We present a method for recognizing se- mantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its im- portance. We improve the accuracy by giving more weights on subtrees that con- tain the predicate and the argument nodes with this ability. Although Kazama and Torisawa (2005) presented fast training with tree kernels, the slow classification during runtime remained to be solved. In this paper, we give a solution that uses an efficient DP updating procedure applica- ble in argument recognition. We demon- strate that the WMOLT kernel improves the accuracy, and our speed-up method makes the recognition more than 40 times faster than the naive classification. 1 Introduction Semantic role labeling (SRL) is a task that recog- nizes the arguments of a predicate (verb) in a sen- tence and assigns the correct role to each argument. As this task is recognized as an important step after (or the last step of) syntactic analysis, many stud- ies have been conducted to achieve accurate seman- tic role labeling (Gildea and Jurafsky, 2002; Mos- chitti, 2004; Hacioglu et al., 2004; Punyakanok et al., 2004; Pradhan et al., 2005a; Pradhan et al., 2005b; Toutanova et al., 2005). Most of the studies have focused on machine learning because of the availability of standard datasets, such as PropBank (Kingsbury and Palmer, 2002). Naturally, the usefulness of parse trees in this task can be anticipated. For example, the recent CoNLL 2005 shared task (Carreras and M`arquez, 2005) provided parse trees for use and their useful- ness was ensured. Most of the methods heuristically extract features from parse trees, and from other sources, and use them in machine learning methods based on feature vector representation. As a result, these methods depend on feature engineering, which is time-consuming. Tree kernels (Collins and Duffy, 2001; Kashima and Koyanagi, 2002) have been proposed to directly handle trees in kernel-based methods, such as SVMs (Vapnik, 1995). Tree kernels calculate the similar- ity between trees, taking into consideration all of the subtrees, and, therefore there is no need for such fea- ture engineering. Moschitti and Bejan (2004) extensively studied tree kernels for semantic role labeling. However, they reported that they could not successfully build an accurate argument recognizer, although the role assignment was improved. Although Moschitti et al. (2005) reported on argument recognition using tree kernels, it was a preliminary evaluation because they used oracle parse trees. Kazama and Torisawa (2005) proposed a new tree kernel for node relation labeling, as which SRL can be cast. This kernel is …

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