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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 2741–2750 of 4002 papers

TitleStatusHype
Word2net: Deep Representations of Language—0
Word2rate: training and evaluating multiple word embeddings as statistical transitions—0
Word2Sense: Sparse Interpretable Word Embeddings—0
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings—0
Word and Document Embeddings based on Neural Network Approaches—0
Word and Phrase Features in Graph Convolutional Network for Automatic Question Classification—0
Word associations and the distance properties of context-aware word embeddings—0
Word-Context Character Embeddings for Chinese Word Segmentation—0
WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers—0
Word Definitions from Large Language Models—0
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