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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 1671–1680 of 4002 papers

TitleStatusHype
Decomposing Word Embedding with the Capsule Network—0
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation—0
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings—0
Analyzing autoencoder-based acoustic word embeddings—0
4chan & 8chan embeddings—0
Adversarial Transfer Learning for Punctuation Restoration—0
DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator—0
Give your Text Representation Models some Love: the Case for BasqueCode0
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n^6) down to O(n^3)Code0
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
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