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.
@article{arxiv.1304.7157,
title = {Question Answering Against Very-Large Text Collections},
author = {Leon Derczynski and Richard Shaw and Ben Solway and Jun Wang},
journal= {arXiv preprint arXiv:1304.7157},
year = {2013}
}