QuestionBank: Creating a Corpus of Parse-Annotated Questions

This paper describes the development of QuestionBank, a corpus of 4000 parseannotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing. We present a series of experiments to investigate the effectiveness of QuestionBank as both an exclusive and supplementary training resource for a state-of-the-art parser in parsing both question and non-question test sets. We introduce a new method for recovering empty nodes and their antecedents (capturing long distance dependencies) from parser output in CFG trees using LFG f-structure reentrancies. Our main findings are (i) using QuestionBank training data improves parser performance to 89.75% labelled bracketing f-score, an increase of almost 11% over the baseline; (ii) back-testing experiments on nonquestion data (Penn-II WSJ Section 23) shows that the retrained parser does not suffer a performance drop on non-question material; (iii) ablation experiments show that the size of training material pr...

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: John Judge, Aoife Cahill, Josef van Genabith
• Keywords: ACL 2006, ACL 2007, Data Improves Parser, Long Distance Dependencies, Parser