[PDF] Top 20 Estimating Linear Models for Compositional Distributional Semantics

... additive compositional dis- tributional semantics ...extracting compositional distributional semantics examples and ...existing compositional distributional ... See full document

... 2012). Compositional distri- butional semantic models (cDSMs) of word units aim at handling, compositionally, the high produc- tivity of phrases and consequent data ...the distributional ... See full document

... of compositional distributional models on this typological similarity ...space models make statistically significant predictions, and the model parame- ters, which had not been optimized for ... See full document

... applying distributional mod- els that assign a graded compositionality score to a phrase, as well as researchers interested in express- ing compositional meaning with such ... See full document

... Figure 1 shows how interaction of feature values for num and VLen in combination with the adjective identity feature can help classify the two ANs containing adjective large as correct (1) or incorrect (-1). In Table 4 ... See full document

... for compositional vector ...the distributional hypoth- esis to multilingual joint-space ...enabling models to abstract away from mono-lingual surface realisations into a deeper se- mantic ... See full document

... of compositional distributional semantic models, and that the seman- tic information contained in these models character- izes which AA will have rigid order (as with rapid social change ... See full document

... Distributional semantics models (DSMs) approx- imate meaning with vectors that record the dis- tributional occurrence patterns of words in cor- ... See full document

... offers compositional abilities to distributional models while at the same time avoids all the above ...additive models of Mitchell and Lapata (2008) on simple transitive sentences, it has a ... See full document

... We take a mathematical look 1 at how the composition operations postulated by CDSMs affect similarity measurements involving the vectors they produce for phrases or sentences. We show that, for an important class of ... See full document

... Compositional distributional semantics models (CDSMs) are functions mapping text fragments to vectors (or higher-order ...map distributional vectors of words to distributional ... See full document

... as Linear Transformations One of the principal claims of the CM hypothesis is that CMs are productive: A CM ...as linear maps in distributional vector ... See full document

... a compositional distri- butional model of ...language semantics which is closer to the ideas underlying standard distributional models of word ... See full document

... In this paper we extend the categorical model of Coecke et al. (2010) in a way to accommodate an information structure layer of composition. In or- der to achieve this, we model intonational bound- aries (the devices for ... See full document

... A matrix is a second-order tensor; for example, the red matrix mentioned earlier lives in the N ⊗ N space, meaning that two indices—each corresponding to a basis vector in the noun space N —are needed to specify an entry ... See full document

... settings CDSMs can only act as components of more com- plex systems, since they require dealing with issues, such as identifying multiword expressions, recognizing named entities, processing dates and digits, or ... See full document

... positional distributional semantic framework in the vein of that of Coecke et ...the compositional mechanism of Baroni and Zamparelli (2010) as a specific case, thereby uniting both lines of research in a ... See full document

... of compositional distributional semantics models estimated for languages other than ...of compositional distributional semantics models of ... See full document

... by linear regression on separate msrvid train data that were also provided by the SemEval task organizers (before combining, we checked that the collinearity between cues was ... See full document

... Table 1 displays correlations between the systems’ scores and human SVO similarity judgements on the verb disambiguation (GS11) and sentence sim- ilarity (KS14) tasks, for both the count (SVD) and prediction vectors ... See full document