Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the development of QuestionBank, a corpus of 4000 parse-annotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing.  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.  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 non-question 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 provided by QuestionBank is sufficient to achieve optimal results; (iv) our method for recovering empty nodes captures long distance dependencies in questions from the ATIS corpus with high precision (96.82%) and low recall (39.38%). In summary, QuestionBank provides a useful new resource in parser-based QA research.
['John Judge', 'Aoife Cahill', 'Josef van Genabith']
I am writing a research paper on a idea QuestionBank: Creating a Corpus of Parse-Annotated Questions
[{"from": "human", "value": "I am writing a research paper on a idea QuestionBank: Creating a Corpus of Parse-Annotated Questions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the development of QuestionBank, a corpus of 4000 parse-annotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing.  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.  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 non-question 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 provided by QuestionBank is sufficient to achieve optimal results; (iv) our method for recovering empty nodes captures long distance dependencies in questions from the ATIS corpus with high precision (96.82%) and low recall (39.38%). In summary, QuestionBank provides a useful new resource in parser-based QA research."}]