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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 1821–1830 of 4002 papers

TitleStatusHype
Structured Pruning of Large Language ModelsCode1
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval TaskCode0
Correlations between Word Vector SetsCode0
On Dimensional Linguistic Properties of the Word Embedding SpaceCode0
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
DialectGram: Detecting Dialectal Variation at Multiple Geographic ResolutionsCode0
Complex networks based word embeddings—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
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