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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 201–210 of 4002 papers

TitleStatusHype
Multimodal Metric Learning for Tag-based Music RetrievalCode1
Multimodal Word DistributionsCode1
NPVec1: Word Embeddings for Nepali - Construction and EvaluationCode1
Null It Out: Guarding Protected Attributes by Iterative Nullspace ProjectionCode1
Obtaining Better Static Word Embeddings Using Contextual Embedding ModelsCode1
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued PretrainingCode1
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word EmbeddingsCode1
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence InferenceCode1
Paraphrase Generation with Latent Bag of WordsCode1
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection TaskCode1
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