Multi-Field Structural Decomposition for Question Answering

TL;DR

This paper introduces a novel multi-field structural decomposition method for question answering that improves document relevance ranking by analyzing linguistic structures and learning field weights, achieving over 40% improvement.

Contribution

It proposes a new approach combining structural linguistic decomposition with statistical learning to enhance question answering accuracy.

Findings

Over 40% improvement over baseline in answer detection.

Effective use of syntactic and semantic trees for document indexing.

Learned field weights significantly boost relevance scoring.

Abstract

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.