Antonio Juárez-González

Learn More
This paper describes the system developed by the Language Technologies Lab at INAOE for the Spanish Question Answering task at CLEF 2006. The presented system is centered in a full data-driven architecture that uses machine learning and text mining techniques to identify the most probable answers to factoid and definition questions respectively. Its major(More)
This paper describes a QA system centered in a full data-driven architecture. It applies machine learning and text mining techniques to identify the most probable answers to factoid and definition questions respectively. Its major quality is that it mainly relies on the use of lexical information and avoids applying any complex language processing resources(More)
This paper introduces the new INAOE's answer validation method. This method is based on supervised learning approach that uses a set of attributes that capture some lexical-syntactic relations among the question, the answer and the given support text. In addition, the paper describes the evaluation of the proposed method at both the Spanish Answer(More)
Although the application of data fusion in information retrieval has yielded good results in the majority of the cases, it has been noticed that its achievement is dependent on the quality of the input result lists. In order to tackle this problem, in this paper we explore the combination of only the n-top result lists as an alternative to the fusion of all(More)
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%,(More)
Some recent works have shown that the " perfect " selection of the best IR system per query could lead to a significant improvement on the retrieval performance. Motivated by this fact, in this paper we focus on the automatic selection of the best retrieval result from a given set of results lists generated by different IR systems. In particular, we propose(More)
  • 1