Mining the web to validate answers to natural language questions

Answer validation is the ability to automatically judge the relevance of a candidate answer with respect to a given question. This paper investigates answer validation following a data-driven approach that considers textual passages (i.e. snippets) retrieved from the Web as the main source of information. Snippets are then analyzed in order to maximize the density of relevant keywords belonging both to the question and to the answer. Results obtained on a corpus of human-judged factoid question-answer pairs submitted by participants at TREC-2001 show a satisfactory degree of success rate (i.e. 86\%). In addition, the efficiency of the methodology (documents are not downloaded) makes the approach suitable to be integrated as a module in the architecture of a question answering system