UCM3: Classification of Semantic Relations between Nominals using Sequential Minimal Optimization

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 382–385, Prague, June 2007. c⃝2007 Association for Computational Linguistics UC3M: Classification of Semantic Relations between Nominals using Sequential Minimal Optimization Isabel Segura Bedmar Computer Science Department University Carlos III of Madrid isegura@inf.uc3m.es Doaa Samy Computer Science Department University Carlos III of Madrid dsamy@inf.uc3m.es Jose L. Martinez Computer Science Department University Carlos III of Madrid jlmartinez@inf.uc3m.es Abstract This paper presents a method for auto- matic classification of semantic relations between nominals using Sequential Minimal Optimization. We participated in the four categories of SEMEVAL task 4 (A: No Query, No Wordnet; B: Word- Net, No Query; C: Query, No WordNet; D: WordNet and Query) and for all train- ing datasets. Best scores were achieved in category B using a set of feature vec- tors including lexical file numbers of nominals obtained from WordNet and a new feature WordNet Vector designed for the task1. 1 Introduction The survey of the state-of-art reveals an increas- ing interest in automatically discovering the un- derlying semantics in natural language. In this interdisciplinary field, the growing interest is justified by the number of applications which can directly benefit from introducing semantic information. Question Answering, Information Retrieval and Text Summarization are examples of these applications (Turney and Littman, 2005; Girju et al., 2005). In the present work and for the purpose of the SEMEVAL task 4, our scope is limited to the semantic relationships between nominals. By this definition, we understand it is the process of discovering the underlying relations between two concepts expressed by two nominals. 1 This work has been partially supported by the Re- gional Government of Madrid Ander the Research Network MAVIR (S-0505/TIC-0267) Within the framework of SEMEVAL, nomi- nals can occur either on the phrase, clause or the sentence level. This fact constitutes the major challenge in this task since most of the previous research limited their approaches to certain types of nominals mainly the “compound nomi- nals”(Girju et al. 2005). The paper is divided as follows; section 2 is a brief introduction to SMO used as the classifier for the task. Section 3 is dedicated to the de- scription of the set of features applied in our ex- periments. In section 4, we discuss the experi- ment’s results compared to the baselines of the SEMEVAL task and the top scores. Finally, we summarize our approach, pointing out conclu- sions and future directions of our work. 2 Sequential Minimal Optimization We decided to use Support Vector Machine (SVM), as one of the most successful Machine Learning techniques, achieving the best per- formances for many classification tasks. Algo- rithm performance and time efficiency are key issues in our task, considering that our final goal is to apply this classification in a Question An- swering System. Sequential Minimal Optimization (SMO) is a fast method to train SVM. SMO breaks the large quadratic programming (QP) optimization prob- lem needed to be resolved in SVM into a series of smallest possible QP problems. These small QP …

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