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According to wikipedia  question answering (QA) is the task of automatically answering a question posed in natural language. To find the answer to a question, a QA computer program may use either a pre-structured database or a collection of natural language documents (a text corpussuch as the World Wide Web or some local collection).

QA research attempts to deal with a wide range of question types including: fact, list, definition, How, Why, hypothetical, semantically-constrained, and cross-lingual questions. Search collections vary from small local document collections, to internal organization documents, to compiled newswire reports, to the World Wide Web.


QA is very dependent on a good search corpus – for without documents containing the answer, there is little any QA system can do. It thus makes sense that larger collection sizes generally lend well to better QA performance, unless the question domain is orthogonal to the collection. The notion of  data redundancy in massive collections, such as the web, means that nuggets of information are likely to be phrased in many different ways in differing contexts and documents, leading to two benefits:

(1) By having the right information appear in many forms, the burden on the QA system to perform complex NLP techniques to understand the text is lessened.
(2) Correct answers can be filtered from false positives by relying on the correct answer to appear more times in the documents than instances of incorrect ones.


*Question answering in Wikipedia. Retrieved 16th June 2009, 20:45 from http://en.wikipedia.org/w/index.php?title=Question_answering&oldid=296213100

*Giuseppe Attardi, Antonio Cisternino, Francesco Formica, Maria Simi, Alessandro Tommasi. PiQaSso: Pisa Questions Answering System. Retrieved 16th June, 2009 20:45 from http://trec.nist.gov/pubs/trec10/papers/piqasso.pdf


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