PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bootstrapping Semantic Analyzers from Non-Contradictory Texts
Ivan Titov and Mikhail Kozhevnikov
In: ACL 2010, 11-16 July 2010, Uppsala, Sweden.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7824
Deposited By:Ivan Titov
Deposited On:17 March 2011