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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 3651–3675 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
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings—0
Dissecting Contextual Word Embeddings: Architecture and Representation—0
Distance Metric Learning for Aspect Phrase Grouping—0
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Distilled Wasserstein Learning for Word Embedding and Topic Modeling—0
Distilling Word Embeddings: An Encoding Approach—0
Distinguishing Japanese Non-standard Usages from Standard Ones—0
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading—0
Distributed Training of Embeddings using Graph Analytics—0
Distributional Analysis of Polysemous Function Words—0
Distributional Hypernym Generation by Jointly Learning Clusters and Projections—0
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