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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 2901–2910 of 4002 papers

TitleStatusHype
Learning Covariate-Specific Embeddings with Tensor Decompositions—0
One-shot and few-shot learning of word embeddings—0
On the Use of Word Embeddings Alone to Represent Natural Language Sequences—0
Lifelong Word Embedding via Meta-Learning—0
Word2net: Deep Representations of Language—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Unsupervised Learning of Entailment-Vector Word Embeddings—0
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Sound Analogies with Phoneme Embeddings—0
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