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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 3651–3660 of 4002 papers

TitleStatusHype
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
Disambiguated skip-gram model—0
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings—0
Discovering Bilingual Lexicons in Polyglot Word Embeddings—0
Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes—0
Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases—0
Discretely Coding Semantic Rank Orders for Supervised Image Hashing—0
Discrete Wavelet Transform for Efficient Word Embeddings and Sentence Encoding—0
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches—0
Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery—0
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