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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 3311–3320 of 4002 papers

TitleStatusHype
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches—0
Can AI Generate Love Advice?: Toward Neural Answer Generation for Non-Factoid Questions—0
Can Domain Adaptation be Handled as Analogies?—0
Can Existing Methods Debias Languages Other than English? First Attempt to Analyze and Mitigate Japanese Word Embeddings—0
Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation?—0
Captioning Images with Novel Objects via Online Vocabulary Expansion—0
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs—0
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models—0
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