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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 2981–2990 of 4002 papers

TitleStatusHype
Semantic Structure and Interpretability of Word EmbeddingsCode0
Deep word embeddings for visual speech recognitionCode0
Topic Based Sentiment Analysis Using Deep Learning—0
One-shot and few-shot learning of word embeddings—0
ALL-IN-1: Short Text Classification with One Model for All LanguagesCode1
Linking Tweets with Monolingual and Cross-Lingual News using Transformed Word Embeddings—0
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis—0
Local Word Vectors Guiding Keyphrase ExtractionCode0
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF—0
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN—0
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