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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 3111–3120 of 4002 papers

TitleStatusHype
NNVLP: A Neural Network-Based Vietnamese Language Processing ToolkitCode0
Handling Homographs in Neural Machine Translation—0
Probabilistic Relation Induction in Vector Space Embeddings—0
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language TasksCode0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Style2Vec: Representation Learning for Fashion Items from Style SetsCode0
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddingsCode0
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