TAILIEUCHUNG - Báo cáo khoa học: "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. . | Bootstrapping Semantic Analyzers from Non-Contradictory Texts Ivan Titov Mikhail Kozhevnikov Saarland University Saarbriicken Germany titov @ Abstract 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. 1 Introduction In recent years there has been increasing interest in statistical approaches to semantic parsing. However most of this research has focused on supervised methods requiring large amounts of labeled data. The supervision was either given in the form of meaning representations aligned with sentences Zettlemoyer and Collins 2005 Ge and Mooney 2005 Mooney 2007 or in a somewhat more relaxed form such as lists of candidate meanings for each sentence Kate and Mooney 2007 Chen and Mooney 2008 or formal representations of the described world state for each text Liang et al. 2009 . Such annotated resources are scarce and expensive to create motivating the need for unsupervised or semi-supervised techniques Poon and Domingos 2009 . However unsupervised methods have their own challenges they are not always able to discover semantic equivalences of lexical entries or logical forms or on the contrary cluster semantically different or even opposite expressions Poon and Domingos 2009 . Unsupervised approaches can only rely on distributional similarity of contexts Harris 1968 to decide on semantic relatedness of terms but this information may be sparse and not .

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