In this work, a data-driven approach for extracting answers from web-snippets is presented. Answers are identified by matching contextual distributional patterns of the expected answer type (EAT) and answer candidates. These distributional patterns are directly learnt from previously annotated tuples {question, sentence, answer}, and the learning mechanism is based on the principles language acquisition. Results shows that this linguistic motivated data-driven approach is encouraging.
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