A Supervised Learning Approach to Spanish Answer Validation

Abstract. This paper describes the results of the INAOE’s answer validation system evaluated at the Spanish track of the AVE 2007. The system is based on a supervised learning approach that considers two kinds of attributes. On the one hand, some attributes indicating the textual entailment between the given support text and the hypothesis constructed from the question and answer. On the other hand, some new features denoting certain answer restrictions as imposed by the question’s type and format. The evaluation results were encouraging; they reached a F-measure of 53% (the best performance in the Spanish track), and outperformed the standard baseline by 15 percentage points.

• Type: Conference
• Year: 2007
• Where: CLEF
• Authors: Alberto Téllez-Valero, Manuel Montes-y-Gómez, Luis Villaseñor Pineda
• Keywords: CLEF 2007, INAOE’s Answer Validation, Information Retrieval, Spanish Track, Supervised Learning Approach