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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 3711–3720 of 4002 papers

TitleStatusHype
Adversarial Training Methods for Semi-Supervised Text ClassificationCode1
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related MessagesCode0
On the Convergent Properties of Word Embedding Methods—0
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks—0
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vecCode0
Stance and Sentiment in Tweets—0
Using Word Embeddings to Translate Named Entities—0
mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic MWE Processing—0
Example-based Acquisition of Fine-grained Collocation Resources—0
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus—0
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