[PDF] Top 20 Combined Distributional and Logical Semantics

... use distributional statistics to determine the proba- bility that a WordNet-derived inference rule is valid in a given ...lexical semantics is integrated into the lexicon, rather than being implemented as ... See full document

... We will sketch a method by which Montague semantics can be described within the context-theoretic framework. We follow a standard method of representing logic in language, but instead of representing words using ... See full document

... multimodal distributional semantics, by first creating their own dataset of images and visual attributes for the nouns contained in the McRae et ...and combined with a purely textual ...of ... See full document

... we combined En- glish names of Chinese districts with names of their capitals (instead of American states and their capitals in the original ...then combined with the related ele- ment according to the ... See full document

... and Distributional Semantics Logic is necessary for implementing the functional aspects of meaning and organizing knowledge in a struc- tured and unambiguous ...butional semantics provides an elegant ... See full document

... Sensitivity to syntactic structure During training, C-PHRASE is sensitive to syntactic structure. To cite an extreme example, boy flowers will be joined in a context-predicting phrase in “these are considered [boy ... See full document

... Consequently, logical approaches have been widely used in semantic parsing where it supports answering complex natural language queries requiring reasoning and data aggregation (Zelle and Mooney 1996; Kwiatkowski ... See full document

... FDS, logical operators are directly expressed as functions over distributions, bypassing existing ...a distributional semantics space to some heavily underspecified form of a “model-theoretic” space ... See full document

... chance (p < 0.05 based on simulated distributions for random cluster assignment). Thus, even alone, image-based vectors do capture aspects of meaning. For AP, adding image features does not improve per- formance, ... See full document

... and Distributional Semantics here advocated suggests a very promising perspective: transformation functions corresponding to different syntactic relations could be learned from suitably processed corpora ... See full document

... lexical semantics that had partly motivated the development of GL (see Pustejovsky’s discussion and criticism of sense enumeration, for example), there was reason to doubt the classic GL model on theoret- ical ... See full document

... be combined with automatic selection of the relevant senses of the compound constituents using the methodology described in Reddy et ...a distributional model of AB semantic transparency, the vector for A ... See full document

... textual distributional models that implement vari- ous standard ways to extract them from a corpus, chosen to be representative of the state of the ...corpora combined (size: ... See full document

... to semantics and discourse (Kamp and Reyle 1993; Ginzburg 2012), and as also recently advocated by McNally and Boleda (to appear), meaning in DS must be modeled in dynamic terms, that is, by considering how each ... See full document

... all contexts, even in contexts like “Sue believes that. . . ” where they should not be exchangeable. Hyper- intensional semantics addresses this problem. In particular, some approaches (Fox and Lappin, 2001, 2005; ... See full document

... in distributional semantics, despite the challenges they face in capturing various semantic ...formal semantics and recent advances in machine ... See full document

... from distributional semantics that “you shall know the word by the company it keeps” (Firth, 1957) is hopelessly confounded in the case of spoken ...capture semantics, we evaluate on speech and ... See full document

... performing parameters from our earlier comparison of different vector sources for distributional seman- tics (Blacoe and Lapata, 2012) where we also used the BNC for training. There we obtained best results with ... See full document

... a distributional model takes the form of a sparse matrix, with entries specified as a triplet of row label (target term), column label (feature term) and co-occurrence frequency ... See full document

... Distributional methods for meaning similarity are based on the observation that similar words oc- cur in similar contexts and measure similarity based on patterns of word occurrence in large cor- pora (Clark, ... See full document