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 1841–1850 of 4002 papers

TitleStatusHype
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Improving neural tagging with lexical information—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings—0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
Show:102550
← PrevPage 185 of 401Next →

No leaderboard results yet.