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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 1961–1970 of 4002 papers

TitleStatusHype
Evaluation of Word Embeddings for the Social Sciences—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC—0
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus—0
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
Evaluation of Question Answering Systems: Complexity of judging a natural language—0
Evaluation of Morphological Embeddings for the Russian Language—0
Evaluation of Greek Word Embeddings—0
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