Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A simple measure to assess non-response
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The effect of entity recognition on answer validation
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
| Hi-index | 12.05 |
ResPubliQA is a Question Answering (QA) evaluation task over European legislation whose first edition was proposed at the Cross Language Evaluation Forum (CLEF) 2009. The exercise consists of extracting a relevant paragraph of text that satisfies the information need expressed by a natural language question. The definition of the task allows to compare current QA technologies with pure Information Retrieval (IR) approaches and to introduce Answer Validation technologies in QA systems. In this paper we describe a system developed for this task. Our system is composed by an IR phase focused on improving QA results, a validation step for removing not promising paragraphs and a module based on n-grams overlapping for selecting the final answer, as well as a selection module that uses Lexical Entailment. While the IR module has contributed to obtain promising results, the performance of the validation module has to be improved. On the other hand, the n-gram ranking improved the results of the ranking given by the IR module.