Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation
Recent developments in distributional semantics (Mikolov, Chen, Corrado, & Dean, 2013;
Mikolov, Sutskever, Chen, Corrado, & Dean, 2013) include a new class of prediction-based …
Mikolov, Sutskever, Chen, Corrado, & Dean, 2013) include a new class of prediction-based …
Predicting human similarity judgments with distributional models: The value of word associations.
S De Deyne, A Perfors, DJ Navarro - Proceedings of COLING …, 2016 - aclanthology.org
Most distributional lexico-semantic models derive their representations based on external
language resources such as text corpora. In this study, we propose that internal language …
language resources such as text corpora. In this study, we propose that internal language …
Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies
In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are
represented as vectors in a high-dimensional vector space. This allows for computing word …
represented as vectors in a high-dimensional vector space. This allows for computing word …
Composition in distributional models of semantics
J Mitchell, M Lapata - Cognitive science, 2010 - Wiley Online Library
Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …
science. The appeal of these models lies in their ability to represent meaning simply by …
More data trumps smarter algorithms: Comparing pointwise mutual information with latent semantic analysis
Computational models of lexical semantics, such as latent semantic analysis, can
automatically generate semantic similarity measures between words from statistical …
automatically generate semantic similarity measures between words from statistical …
[HTML][HTML] A comparative evaluation and analysis of three generations of Distributional Semantic Models
Distributional semantics has deeply changed in the last decades. First, predict models stole
the thunder from traditional count ones, and more recently both of them were replaced in …
the thunder from traditional count ones, and more recently both of them were replaced in …
A critique of word similarity as a method for evaluating distributional semantic models
This paper aims to re-think the role of the word similarity task in distributional semantics
research. We argue while it is a valuable tool, it should be used with care because it …
research. We argue while it is a valuable tool, it should be used with care because it …
[HTML][HTML] The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics
G Hollis, C Westbury - Psychonomic bulletin & review, 2016 - Springer
Notable progress has been made recently on computational models of semantics using
vector representations for word meaning (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov …
vector representations for word meaning (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov …
[PDF][PDF] The distributional hypothesis
M Sahlgren - Italian Journal of linguistics, 2008 - diva-portal.org
Distributional approaches to meaning acquisition utilize distributional properties of linguistic
entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of …
entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of …
[PDF][PDF] Don't count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
Context-predicting models (more commonly known as embeddings or neural language
models) are the new kids on the distributional semantics block. Despite the buzz …
models) are the new kids on the distributional semantics block. Despite the buzz …