Multi-Field Structural Decomposition for Question Answering
This addresses the problem of improving document retrieval for question answering, though it appears incremental as it builds on structural decomposition methods.
The paper tackles question answering by decomposing linguistic structures into multiple fields and indexing them, achieving over 40% absolute improvement in detecting answer-containing documents compared to a baseline simple search.
This paper presents a precursory yet novel approach to the question answering task using structural decomposition. Our system first generates linguistic structures such as syntactic and semantic trees from text, decomposes them into multiple fields, then indexes the terms in each field. For each question, it decomposes the question into multiple fields, measures the relevance score of each field to the indexed ones, then ranks all documents by their relevance scores and weights associated with the fields, where the weights are learned through statistical modeling. Our final model gives an absolute improvement of over 40% to the baseline approach using simple search for detecting documents containing answers.