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- W1565175571 abstract "Anticipating the availability of large question-answerdatasets, we propose a principled, data-drivenInstance-Based approach to Question Answering.Most question answering systems incorporatethree major steps: classify questions accordingto answer types, formulate queries for documentretrieval, and extract actual answers. Under our approach,strategies for answering new questions aredirectly learned from training data. We learn modelsof answer type, query content, and answer extractionfrom clusters of similar questions. We viewthe answer type as a distribution, rather than a classin an ontology. In addition to query expansion, welearn general content features from training data anduse them to enhance the queries. Finally, we treatanswer extraction as a binary classification problemin which text snippets are labeled as correct or incorrectanswers. We present a basic implementationof these concepts that achieves a good performanceon TREC test data." @default.
- W1565175571 created "2016-06-24" @default.
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- W1565175571 date "2004-07-01" @default.
- W1565175571 modified "2023-09-25" @default.
- W1565175571 title "Instance-Based Question Answering: A Data Driven Approach" @default.
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- W1565175571 doi "https://doi.org/10.1184/r1/6606515.v1" @default.
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