Bootstrapping Semantic Analyzers from Non-Contradictory Texts

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Bootstrapping Semantic Analyzers from Non-Contradictory Texts
We argue that groups of unannotated texts with overlapping and non-contradictory semantics represent a valuable source of information for learning semantic representations. A simple and efficient inference method recursively induces joint semantic representations for each group and discovers correspondence between lexical entries and latent semantic concepts. We consider the generative semantics-text correspondence model (Liang et al., 2009) and demonstrate that exploiting the noncontradiction relation between texts leads to substantial improvements over natural baselines on a problem of analyzing human-written weather forecasts.
Ivan Titov, Mikhail Kozhevnikov
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Ivan Titov, Mikhail Kozhevnikov
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