[HTML][HTML] Semantic memory: A review of methods, models, and current challenges
AA Kumar - Psychonomic Bulletin & Review, 2021 - Springer
Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …
system that consists of knowledge about the world, concepts, and symbols. Considerable …
Vector-space models of semantic representation from a cognitive perspective: A discussion of common misconceptions
Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …
Evaluating word embedding models: Methods and experimental results
Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …
processing applications is conducted in this work. First, we introduce popular word …
[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 …
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 …
Multimodal distributional semantics
Distributional semantic models derive computational representations of word meaning from
the patterns of co-occurrence of words in text. Such models have been a success story of …
the patterns of co-occurrence of words in text. Such models have been a success story of …
A survey of word embeddings evaluation methods
A Bakarov - arXiv preprint arXiv:1801.09536, 2018 - arxiv.org
Word embeddings are real-valued word representations able to capture lexical semantics
and trained on natural language corpora. Models proposing these representations have …
and trained on natural language corpora. Models proposing these representations have …
Combining language and vision with a multimodal skip-gram model
We extend the SKIP-GRAM model of Mikolov et al.(2013a) by taking visual information into
account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based …
account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based …
Distributional memory: A general framework for corpus-based semantics
Research into corpus-based semantics has focused on the development of ad hoc models
that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled …
that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled …