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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 2471–2480 of 4002 papers

TitleStatusHype
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification—0
Auto-Encoding Dictionary Definitions into Consistent Word EmbeddingsCode0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Siamese Network-Based Supervised Topic Modeling—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
Similarity-Based Reconstruction Loss for Meaning Representation—0
Parameter-free Sentence Embedding via Orthogonal BasisCode0
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
BLISS in Non-Isometric Embedding Spaces—0
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