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a list of questions that are frequently asked (about a given topic) along with their answers

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Assuming that an answer to a user question exists in a FAQ file and that the system returns 5 QA pairs, FAQ FINDER will perform at either 100-percent recall and 20-percent precision (if the answer is retrieved), or 0-percent recall and 0-percent precision (if it is not).
If the rejection threshold is set too high, some correct answers will be eliminated; however, if the threshold is too low, then incorrect responses will often be given to the user when no answer exists in the FAQ file.
In the Investment FAQ file, the following text can be found:
However, it is also useful to be informed that an answer does not exist within a FAQ file, possibly suggesting to the user that the question should be submitted to the FAQ's related news group.
We know from our evaluation that if an answer is present in the FAQ file, the system is likely to find it.
These assumptions do not hold for all FAQ files, as we discuss later, but they hold often enough to form a good starting point for research.
FAQ FINDER compares the question to its set of FAQ files, returning a list of files ranked by their relevance to the question, including, in this case, the file auto_consumer_FAQ as the most relevant file.
The first step is to narrow the search to a small set of FAQ files likely to contain an answer to the user's question.
It then forms a term vector from the query, which is matched against similar vectors already created for the FAQ files in an offline indexing step.
This metric does not require any understanding of the text - a good thing because the answers in FAQ files are free natural language text, often several paragraphs or more in length.
There are other more difficult cases of ellipsis found in FAQ files.
An important part of maintaining the performance of FAQ FINDER on a large set of FAQ files will be the incorporation of new vocabulary items into WORDNET.