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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 2641–2650 of 4002 papers

TitleStatusHype
Using Word Embeddings to Analyze Protests News—0
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Using word embeddings to improve the discriminability of co-occurrence text networks—0
Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News—0
Using Word Embeddings to Translate Named Entities—0
Using Word Embeddings to Uncover Discourses—0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research—0
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models—0
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