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 3261–3270 of 4002 papers

TitleStatusHype
Adversarial Training for Unsupervised Bilingual Lexicon Induction—0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Neural Question Answering at BioASQ 5B—0
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context—0
Jointly Learning Word Embeddings and Latent Topics—0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
A Mixture Model for Learning Multi-Sense Word Embeddings—0
A Survey Of Cross-lingual Word Embedding Models—0
Query-by-Example Search with Discriminative Neural Acoustic Word EmbeddingsCode0
Neural Domain Adaptation for Biomedical Question AnsweringCode0
Show:102550
← PrevPage 327 of 401Next →

No leaderboard results yet.