[PDF] Top 20 A Structured Distributional Semantic Model : Integrating Structure with Semantics

... However, semantics in natural language is a compositional phenomenon, encom- passing interactions between syntactic structures, and the meaning of lexical ... See full document

... SDSM matrices for composed concepts. However, are these correct? Intuitively, if they truly capture semantics, the two SDSM matrix representations for “Booth assassinated Lincoln” and “Booth shot Lincoln with a ... See full document

... Structured distributional semantic models aim to improve upon simple vector space models of semantics by hypothesizing that the meaning of a word is captured more effectively through its ... See full document

... of distributional semantics, ...using structured representations of linguistic contexts over bag-of- words ones ...While structured models have been shown to outperform the latter in a number ... See full document

... information structure for the emerging field of categorical compositional distributional se- ...a model capable of accommodating two different types of composition over a distributional ...the ... See full document

... occur. Distributional models capture the graded nature of meaning, but do not adequately capture log- ical structure (Grefenstette, ...2011). Distributional similarity is usually a mix- ture of ... See full document

... tional semantic (DS) models able to provide such a method, in virtue of (i) TCL’s distinc- tion between internal or conceptual content and external or referential content and (ii) the close correspondence between ... See full document

... tionality suggested by our approach. Modelling compositionality as a machine learning task im- plies that a great number of different “types” of composition (functions combining vectors) may be learned from natural ... See full document

... of semantic similarity pro- vided by the distributional semantic model, it is also possible to properly test the hypothesis that productivity is tied to the structure of the seman- tic ... See full document

... computational model of semantic complexity in sentence processing, which is strongly inspired by ...Our model integrates various insights from current research in distributional ... See full document

... so-called distributional hypothesis that words that occur in similar contexts tend to have similar meanings, and to various proposals for how to implement this hypothesis (Curran, 2004), including alternative ... See full document

... The work presented in this paper is a first attempt at developing a distributional model of coercion interpretation grounded in the theory of perceptual affordances. The idea proposed and provisionally ... See full document

... and semantic vectors derived from a distributional se- mantic model, the Nadaraya-Watson estimator can estimate the position in the semantic vector space for each word in the ...Context ... 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 ...visual semantic space is then represented by a ... See full document

... more semantic relations, and indeed more synonyms, are extracted by the Unigram Word Space than the Multiword Term ...to model multiword terms in a distributional framework and to handle ... See full document

... In brief, we aimed to highlight the ongoing effort to address some of these points, either by theoretical reasoning, or through example via demonstrating interesting properties of new or existing distributional ... See full document

... [r] ... See full document

... alone. Textual information in this case is not com- plementary to visual information, but simply poorer. Also note that LAB features do better than SIFT features. This is probably due to the fact that Exper- iment 1 is ... See full document

... Distributional semantics has revolutionised com- putational semantics by representing the meaning of linguistic expressions as vectors that capture their co-occurrence patterns in large corpora (Tur- ... See full document

... Furthermore, since sparse representations have been shown to be beneficial in NLP, both for ap- plications and for interpretability of features (Mur- phy et al., 2012; Faruqui et al., 2015), we can en- force sparsity in ... See full document