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 1891–1900 of 4002 papers

TitleStatusHype
Interpretable Word Embedding Contextualization—0
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces—0
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
Derivational Morphological Relations in Word Embeddings—0
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function—0
Cross-lingual Transfer of Sentiment Classifiers—0
Derivational Morphological Relations in Word Embeddings—0
Show:102550
← PrevPage 190 of 401Next →

No leaderboard results yet.