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Large scale Semantic Parsing via Schema Matching and Lexicon Extension

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Figure 1: We reduce the task of learning a large-scale semantic parser to a combination of 1) astandard supervised algorithm for learning seman-tic parsers; 2) our MATCHER algorithm for find-ing correspondences between words and databasesymbols; and 3) our LEXTENDER algorithm forintegrating (word, database symbol) matches intoa semantic parsing lexicon.
Table 1: Patterns used by MATCHERrandomly selected from the tuples in as evidence of a match between rD and rT
Table 2: MATCHERΦ =1 corresponds to statistics: for each κ func-tion for comparing two tuples (given in Table 3),MATCHER computes the statistics above to com-pare rD and rT
Figure 3: MATCHER’s Pattern features and Extrac-tions features complement one another, so that incombination they outperform either subset on itsown, especially at the high-recall end of the curve.
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