# Question Answering Against Very-Large Text Collections

Source: [Question Answering Against Very-Large Text Collections](<https://www.sota2.com/research/paper/68fa60f01e209131ce9f1c2b>)

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

Data updated: 2025-10-25T19:49:34.015Z

Authors: Leon Derczynski, Richard Shaw, Ben Solway, Jun Wang

Published: 2013-04-26T13:27:19.000Z

## Abstract

Question answering involves developing methods to extract useful information from large collections of documents. This is done with specialised search engines such as Answer Finder. The aim of Answer Finder is to provide an answer to a question rather than a page listing related documents that may contain the correct answer. So, a question such as "How tall is the Eiffel Tower" would simply return "325m" or "1,063ft". Our task was to build on the current version of Answer Finder by improving information retrieval, and also improving the pre-processing involved in question series analysis.

## 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/1304.7157>)

[PDF](<https://arxiv.org/pdf/1304.7157.pdf>)