Detecting expected answer relations through textual entailment
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Combining lexical resources with tree edit distance for recognizing textual entailment
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Expected Answer Type Identification from Unprocessed Noisy Questions
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Towards Extensible Textual Entailment Engines: The EDITS Package
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Fuzzy-Based Answer Ranking in Question Answering Communities
International Journal of Digital Library Systems
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This paper reports on experiments performed in the development of the QALL-ME system, a multilingual QA infrastructure capable of handling input requests both in written and spoken form. Our objective is to estimate the impact of dealing with automatically transcribed (i.e.noisy) requests on a specific question interpretation task, namely the extraction of relations from natural language questions. A number of experiments are presented, featuring different combinations of manually and automatically transcribed questions datasets to train and evaluate the system. Results (ranging from 0.624 to 0.634 F-measure in the recogniton of the relations expressed by a question) demonstrate that the impact of noisy data on question interpretation is negligible with all the combinations of training/test data. This shows that the benefits of enabling speech access capabilities, allowing for a more natural human-machine interaction, outweight the minimal loss in terms of performance.