Distributional Semantic Models
EVALution 1 0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models
... of Distributional Semantic Models ...several semantic relations between word pairs (in- cluding hypernymy, synonymy, antonymy, ...word semantic field, ...
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Crossmodal Network Based Distributional Semantic Models
... of distributional semantic models (DSMs) in various semantic tasks they remain disconnected with real-world perceptual cues since they typically rely on linguistic ...such models, ...
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Low Dimensional Manifold Distributional Semantic Models
... of semantic similarity between words, sentences and documents is a fundamental problem for many research disciplines including computational linguistics (Malandrakis et ...2011), semantic web (Corby et ...
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Distributional semantic models for the evaluation of disordered language
... Atypical semantic and pragmatic expression is frequently reported in the language of children with ...use distributional semantic models to automat- ically identify unexpected words in ...
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Conceptual Change and Distributional Semantic Models: an Exploratory Study on Pitfalls and Possibilities
... apply distributional semantic models to study a complex phenomenon such as concept drift in a methodologically sound ...tributional semantic models reflect the way words are used and ...
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Fusion of Compositional Network based and Lexical Function Distributional Semantic Models
... Distributional Semantic Models (DSMs) have been successful at modeling the meaning of individual words, with interest recently shift- ing to compositional structures, ...
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Factorization of Latent Variables in Distributional Semantic Models
... Distributional Semantic Models (DSMs) have be- come standard paraphernalia in the natural lan- guage processing toolbox, and even though there is a wide variety of models available, the basic ...
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A SICK cure for the evaluation of compositional distributional semantic models
... Distributional Semantic Models (DSMs) approximate the meaning of words with vectors summarizing their pat- terns of co-occurrence in ...the distributional representations of the words they ...
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Vectors or Graphs? On Differences of Representations for Distributional Semantic Models
... Distributional Semantic Models (DSMs) have recently received increased attention, together with the rise of neural architectures for scalable training of dense vector ...vector-based models ...
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An Artificial Language Evaluation of Distributional Semantic Models
... The distributional tradition in linguistics ...Modern distributional semantic models (DSMs) formalize this process to construct vector representations for word meaning from statistical ...
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The Effects of Data Size and Frequency Range on Distributional Semantic Models
... Distributional Semantic Models (DSMs) have be- come a staple in natural language ...cessing models — matrix-based models, neural net- works, and hashing methods — have also enjoyed ...
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A corpus based evaluation method for Distributional Semantic Models
... Distributional Semantic Models (DSM) can be traced back to the hypothesis proposed by Harris (1954) whereby the meaning of a word can be in- ferred from its ...plementations. Models evaluation ...
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CCG Categories for Distributional Semantic Models
... The distributional semantic approach is based on the idea that the meaning of a word relies heavily on its ...based Distributional Semantic Models (DSMs) have been tested against ...
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Literal and Metaphorical Senses in Compositional Distributional Semantic Models
... Compositional distributional semantic models (CDSMs) provide a compact model of composi- tionality that produces vector representations of phrases while avoiding the sparsity and storage issues ...
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Inference with Distributional Semantic Models
... formal models lack the large-scale inductive power of distributional semantic ...their distributional representations to Boolean vectors, enforcing feature inclusion in Boolean space for the ...
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Detecting linguistic idiosyncratic interests in autism using distributional semantic models
... using distributional semantic models. We model the semantic space of children’s narratives by calculat- ing pairwise word overlap, and we com- pare the overlap found within and across ...
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Lexical Variability and Compositionality: Investigating Idiomaticity with Distributional Semantic Models
... the models employed in this first ...the models the suceeded the most in placing id- ioms before non-idioms in the obtained rankings and exhibited the best trade-off between precision and recall, as shown ...
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A critique of word similarity as a method for evaluating distributional semantic models
... Another issue with existing word similarity data sets is their small size. This ranges from 30 to 3000 data points (Miller and Charles, 1991; Rubenstein and Goodenough, 1965; Landauer and Dumais, 1997; Finkelstein et ...
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BrainBench: A Brain Image Test Suite for Distributional Semantic Models
... regression models that took brain images as input and predicted the dimensions of a DS model as out- ...regression models for all 1770 pairs of words takes hours to complete, whereas the test we suggest ...
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Semantic Similarity Computation for Abstract and Concrete Nouns Using Network based Distributional Semantic Models
... Here, we summarize the main ideas of network-based DSMs as proposed in [Iosif and Potamianos (2013)]. The network is defined as an undirected (under a symmetric similarity metric) graph F = (V, E) whose the set of ...
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