Semantic Role Labeling Via Generalized Inference Over Classifiers

Semantic Role Labeling Via Generalized Inference Over Classifiers Vasin Punyakanok, Dan Roth, Wen-tau Yih, Dav Zimak Yuancheng Tu Department of Computer Science Department of Linguistics University of Illinois at Urbana-Champaign {punyakan,danr,yih,davzimak,ytu}@uiuc.edu Abstract We present a system submitted to the CoNLL- 2004 shared task for semantic role labeling. The system is composed of a set of classifiers and an inference procedure used both to clean the classification results and to ensure struc- tural integrity of the final role labeling. Lin- guistic information is used to generate features during classification and constraints for the in- ference process. 1 Introduction Semantic role labeling is a complex task to discover pat- terns within sentences corresponding to semantic mean- ing. We believe it is hopeless to expect high levels of per- formance from either purely manual classifiers or purely learned classifiers. Rather, supplemental linguistic infor- mation must be used to support and correct a learning system. The system we present here is composed of two phases. First, a set of phrase candidates is produced using two learned classifiers—one to discover beginning positions and one to discover end positions for each argument type. Hopefully, this phase discovers a small superset of all phrases in the sentence (for each verb). In the second phase, the final prediction is made. First, candidate phrases from the first phase are re-scored using a classifier designed to determine argument type, given a candidate phrase. Because phrases are considered as a whole, global properties of the candidates can be used to discover how likely it is that a phrase is of a given ar- gument type. However, the set of possible role-labelings is restricted by structural and linguistic constraints. We encode these constraints using linear functions and use integer programming to ensure the final prediction is con- sistent (see Section 4). 2 SNoW Learning Architecture The learning algorithm used is a variation of the Winnow update rule incorporated in SNoW (Roth, 1998; Roth and Yih, 2002), a multi-class classifier that is specifically tai- lored for large scale learning tasks. SNoW learns a sparse network of linear functions, in which the targets (phrase border predictions or argument type predictions, in this case) are represented as linear functions over a common feature space. It incorporates several improvements over the basic Winnow update rule. In particular, a regular- ization term is added, which has the affect of trying to separate the data with a think separator (Grove and Roth, 2001; Hang et al., 2002). In the work presented here we use this regularization with a fixed parameter. Experimental evidence has shown that SNoW activa- tions are monotonic with the confidence in the prediction Therefore, it can provide a good source of probability es- timation. We use softmax (Bishop, 1995) over the raw ac- tivation values as conditional probabilities. Specifically, suppose the number of classes is n, and the raw activa- tion values of class i is acti. The posterior estimation for class i is derived by the following equation. score(i) = pi = eacti � …

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