Question Answering Against Very-Large Text Collections
This work addresses the problem of providing direct answers rather than document lists for users of question answering systems, but it appears incremental as it builds on an existing version of Answer Finder.
The paper tackled improving question answering from large text collections by enhancing information retrieval and pre-processing for question series analysis in the Answer Finder system, aiming to return direct answers like '325m' for queries such as 'How tall is the Eiffel Tower'.
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.