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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 871–880 of 4002 papers

TitleStatusHype
Crossword: Estimating Unknown Embeddings using Cross Attention and Alignment Strategies—0
First Bilingual Word Embeddings for te reo Māori and English: Towards Code-switching Detection in a Low-resourced setting—0
On the interpretability and significance of bias metrics in texts: a PMI-based approach—0
Discrete Wavelet Transform for Efficient Word Embeddings and Sentence Encoding—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
Multi-Stage Framework with Refinement based Point Set Registration for Unsupervised Bi-Lingual Word Alignment—0
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions—0
Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
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