Bootstrapping Semantic Analyzers from Non-Contradictory Texts

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 958–967, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Bootstrapping Semantic Analyzers from Non-Contradictory Texts Ivan Titov Mikhail Kozhevnikov Saarland University Saarbr¨ucken, Germany {titov|m.kozhevnikov}@mmci.uni-saarland.de Abstract We argue that groups of unannotated texts with overlapping and non-contradictory semantics represent a valuable source of information for learning semantic repre- sentations. A simple and efficient infer- ence method recursively induces joint se- mantic representations for each group and discovers correspondence between lexical entries and latent semantic concepts. We consider the generative semantics-text cor- respondence model (Liang et al., 2009) and demonstrate that exploiting the non- contradiction relation between texts leads to substantial improvements over natu- ral baselines on a problem of analyzing human-written weather forecasts. 1 Introduction In recent years, there has been increasing inter- est in statistical approaches to semantic parsing. However, most of this research has focused on su- pervised methods requiring large amounts of la- beled 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 some- what more relaxed form, such as lists of candidate meanings for each sentence (Kate and Mooney, 2007; Chen and Mooney, 2008) or formal repre- sentations 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 tech- niques (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 reliable (Weeds and Weir, 2005). For example, when analyzing weather forecasts it is very hard to discover in an unsupervised way which of the expressions among “south wind”, “wind from west” and “southerly” denote the same wind direction and which are not, as they all have a very similar distribution of their contexts. The same challenges affect the problem of identification of argument roles and predicates. In this paper, we show that groups of unanno- tated texts with overlapping and non-contradictory semantics provide a valuable source of informa- tion. This form of weak supervision helps to discover implicit clustering of lexical entries and predicates, which presents a challenge for purely unsupervised techniques. We assume that each text in a group is independently generated from a full latent semantic state corresponding to the group. Importantly, the texts in each group do not have to be paraphrases of each other, as they can verbalize only specific parts (aspects) of the full semantic state, yet statements about the same aspects must not contradict each other. Simulta- neous inference of the semantic state for the non- contradictory and semantically overlapping docu- …

Content truncated to 500 words