Microsoft Research Redmond participated for the first time in TREC this year, focusing on the question answering track.\u00a0 There is a separate report in this volume on the Microsoft Research Cambridge submissions for the filtering and Web tracks (Robertson et al., 2002).\u00a0 We have been exploring data-driven techniques for Web question answering, and modified our system somewhat for participation in TREC QA.\u00a0 We submitted two runs for the main QA track (AskMSR and AskMSR2).<\/p>\n
Data-driven methods have proven to be powerful techniques for natural language processing.\u00a0 It is still unclear to what extent this success can be attributed to specific techniques, versus simply the data itself.\u00a0 For example, Banko and Brill (2001) demonstrated that for confusion set disambiguation, a prototypical disambiguation-in-string-context problem, the amount of data used far dominates the learning method employed in improving labeling accuracy.\u00a0 The more training data that is used, the greater the chance that a new sample being processed can be trivially related to samples appearing in the training data, thereby lessening the need for any complex reasoning that may be beneficial in cases of sparse training data.<\/p>\n
The idea of allowing the data, instead of the methods, do most of the work is what motivated our particular approach to the TREC Question Answering task.\u00a0 One of the biggest challenges in TREC-style QA is overcoming the surface string mismatch between the question formulation and the string containing its answer.\u00a0 For some Question\/Answer pairs, deep reasoning is needed to relate the two.\u00a0 The larger the data set from which we can draw answers, the greater the chance we can find an answer that holds a simple, easily discovered relationship to the query string.<\/p>\n
Our approach to question answering is to take advantage of the vast amount of text data that is now available online.\u00a0 In contrast to many question answering systems that begin with rich linguistic resources (e.g., parsers, dictionaries, WordNet), we begin with data and use that to drive the design of our system.\u00a0 To do this, we first use simple techniques to look for answers to questions on the Web.\u00a0 Since the Web has orders of magnitude more data than the TREC QA document collection, simple techniques are likely to work here.\u00a0 After we have found suitable answer strings from online text, we project them onto the TREC corpus in search of supporting documents<\/p>\n","protected":false},"excerpt":{"rendered":"
Microsoft Research Redmond participated for the first time in TREC this year, focusing on the question answering track.\u00a0 There is a separate report in this volume on the Microsoft Research Cambridge submissions for the filtering and Web tracks (Robertson et al., 2002).\u00a0 We have been exploring data-driven techniques for Web question answering, and modified our 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