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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 1951–1960 of 4002 papers

TitleStatusHype
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
Investigating Gender Bias in BERT—0
Investigating Graph Structure Information for Entity Alignment with Dangling Cases—0
Investigating Language Universal and Specific Properties in Word Embeddings—0
Investigating neural architectures for short answer scoring—0
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian—0
Automatic classification of speech overlaps: Feature representation and algorithms—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
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