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.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Ivan Titov, Mikhail Kozhevnikov
• Keywords: ACL 2010, Computational Linguistics, Joint Semantic Representations, Semantic, Semantic Representations