Inferring Semantic Roles Using Sub-Categorization Frames and Maximum Entropy Model

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 165–168, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Inferring semantic roles using sub-categorization frames and maximum entropy model Akshar Bharati, Sriram Venkatapathy and Prashanth Reddy Language Technologies Research Centre, IIIT - Hyderabad, India. {sriram,prashanth}@research.iiit.ac.in Abstract 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 CONLL-2005 shared task. We show that this approach per- forms well in comparison to an ap- proach which uses only the maximum entropy model. 1 Introduction Semantic role labelling is the task of assigning appropriate semantic roles to the arguments of a verb. The semantic role information is impor- tant for various applications in NLP such as Ma- chine Translation, Question Answering, Informa- tion Extraction etc. In general, semantic role in- formation is useful for sentence understanding. We submitted our system for closed challenge at CONLL-2005 shared task. This task encour- ages participants to use novel machine learning techniques suited to the task of semantic role la- belling. Previous approaches on semantic role labelling can be classified into three categories (1) Explicit Probabilistic methods (Gildea and Jurafsky, 2002). (2) General machine learning algorithms (Pradhan et al., 2003) (Lim et al., 2004) and (3) Generative model (Thompson et al., 2003). Our approach has two stages; first, identifica- tion whether the argument is mandatory or op- tional and second, the classification or labelling of the arguments. In the first stage, the arguments of a verb are put into three classes, (1) mandatory, (2) optional or (3) null. Null stands for the fact that the constituent of the verb in the sentence is not an semantic argument of the verb. It is used to rule out the false argument of the verb which were obtained using the parser. The maximum entropy based classifier is used to classify the arguments into one of the above three labels. After obtaining information about the nature of the non-null arguments, we proceed in the second stage to classify the mandatory and optional ar- guments into their semantic roles. The propbank sub-categorization frames are used to assign roles to the mandatory arguments. For example, in the sentence ”John saw a tree”, the sub-categorization frame ”A0 v A1” would assign the roles A0 to John and A1 to tree respectively. After …

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