Special section: Information Retrieval and Natural Language Processing

 

TrainQA: a Training Corpus for Corpus–Based Question Answering Systems

 

David Tomás¹, José L. Vicedo¹, Empar Bisbal², and Lidia Moreno²

 

¹ Department of Software and Computing Systems, University of Alicante, Spain. (dtomas@dlsi.ua.es, vicedo@dlsi.ua.es)

² Department of Information Systems and Computation, Technical University of Valencia, Spain. (ebisbal@dsic.upv.es, lmoreno@dsic.upv.es)

 

Manuscript received November 23, 2008.
Manuscript accepted for publication August 15, 2009.

 

Abstract

This paper describes the development of an English corpus of factoid TREC–like question–answer pairs. The corpus obtained consists of more than 70,000 samples, containing each one the following information: a question, its question type, an exact answer to the question, the different contexts levels (sentence, paragraph and document) where the answer occurs inside a document, and a label indicating whether the answer is correct (a positive sample) or not (a negative sample). For instance, TrainQA can be used for training a binary classifier in order to decide if a given answer is correct (positive) to the question formulated or not (negative). To our knowledge, this is the first corpus aimed to train on every stage of a trainable Question Answering system: question classification, information retrieval, answer extraction and answer validation.

Key words: Question answering, corpus–based systems.

 

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ACKNOWLEDGEMENT

This work has been developed in the framework of the project CICYT R2D2 (TIC2003–07158–C04).

 

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