SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1591–1600 of 4002 papers

TitleStatusHype
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?Code0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
All Word Embeddings from One EmbeddingCode1
On Adversarial Examples for Biomedical NLP Tasks—0
Upgrading the Newsroom: An Automated Image Selection System for News Articles—0
Revisiting the Context Window for Cross-lingual Word Embeddings—0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT—0
Effect of Text Color on Word Embeddings—0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Show:102550
← PrevPage 160 of 401Next →

No leaderboard results yet.