Paper: Inferring Semantic Roles Using Sub-Categorization Frames And Maximum Entropy Model

ACL ID W05-0621
Title Inferring Semantic Roles Using Sub-Categorization Frames And Maximum Entropy Model
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

In this paper, we propose an approach for inferring semantic role using sub- categorization frames and maximum entropy model. Our approach aims to use the sub-categorization information of the verb to label the mandatory ar- guments of the verb in various possi- ble ways. The ambiguity between the assignment of roles to mandatory argu- ments is resolved using the maximum entropy model. The unlabelled manda- tory arguments and the optional argu- ments are labelled directly using the maximum entropy model such that their labels are not one among the frame el- ements of the sub-categorization frame used. Maximum entropy model is pre- ferred because of its novel approach of smoothing. Using this approach, we obtained an F-measure of 68.14% on the development set of the data provided for the CO...