# Multi-Field Structural Decomposition for Question Answering

Source: [Multi-Field Structural Decomposition for Question Answering](<https://www.sota2.com/research/paper/68fa625f1e209131ce9f8e41>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:18:31.734Z

Authors: Tomasz Jurczyk, Jinho D. Choi

Published: 2016-04-04T16:33:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1604.00938>)

[PDF](<https://arxiv.org/pdf/1604.00938.pdf>)