Analyzing the Use of Non-overlap Features for Supervised Answer Validation

Abstract

Abstract. This year we evaluated our supervised answer validation method at both, the Spanish Answer Validation Exercise (AVE) and the Spanish Question Answering Main Task. This paper describes and analyzes our evaluation results from both tracks. In resume, the F-measure of the proposed method outperformed the baseline result of the AVE 2008 task by more than 100%, and enhanced the performance of our question answering system, showing a gain in accuracy of 22% for answering factoid questions. A detailed analysis of the results shows that the proposed non-overlap features are most discriminative than the traditional overlap ones. In particular, these novel features allowed increasing the F-measure result of our method by 26%.

When the answer comes into question in question-answering: survey and open issues

“…For instance, Iftene and Balahur (2008) compute a fitness value based on the matching between the named entities on the hypothesis and the entities of the supporting text.Based on machine learning – using a training corpus and a set of features, such as the Levenshtein distance or the size of the longest common subsequence between the hypothesis and the supporting snippet (Kozareva, Vazquez and Montoyo 2006; Tèllez-Valero et al . 2008). …”

“…Based on machine learning – using a training corpus and a set of features, such as the Levenshtein distance or the size of the longest common subsequence between the hypothesis and the supporting snippet (Kozareva, Vazquez and Montoyo 2006; Tèllez-Valero et al . 2008).…”