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 2611–2620 of 4002 papers

TitleStatusHype
Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users—0
Using bilingual word-embeddings for multilingual collocation extraction—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations—0
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism—0
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese—0
How Can BERT Help Lexical Semantics Tasks?—0
Using Embedding Masks for Word Categorization—0
Using Gaze Data to Predict Multiword Expressions—0
Using k-way Co-occurrences for Learning Word Embeddings—0
Show:102550
← PrevPage 262 of 401Next →

No leaderboard results yet.