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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 3071–3080 of 4002 papers

TitleStatusHype
On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings—0
On the contribution of word embeddings to temporal relation classification—0
On the Convergent Properties of Word Embedding Methods—0
On the Correlation of Word Embedding Evaluation Metrics—0
On the Cross-lingual Transferability of Contextualized Sense Embeddings—0
On the Curious Case of _2 norm of Sense Embeddings—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
Bridging Vision and Language Spaces with Assignment PredictionCode0
Co-occurrences using Fasttext embeddings for word similarity tasks in UrduCode0
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