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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 1171–1180 of 4002 papers

TitleStatusHype
Towards Multi-Sense Cross-Lingual Alignment of Contextual EmbeddingsCode0
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings—0
Evaluation of Morphological Embeddings for the Russian Language—0
CNN-based Spoken Term Detection and Localization without Dynamic Programming—0
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification—0
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning—0
Overcoming Poor Word Embeddings with Word Definitions—0
Lex2vec: making Explainable Word Embeddings via Lexical Resources—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Spanish Biomedical and Clinical Language Embeddings—0
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