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 2571–2580 of 4002 papers

TitleStatusHype
Enhancing General Sentiment Lexicons for Domain-Specific Use—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
Cross-Document Narrative Alignment of Environmental News: A Position Paper on the Challenge of Using Event Chains to Proxy Narrative Features—0
Identifying Aggression and Toxicity in Comments using Capsule Network—0
Learning Diachronic Analogies to Analyze Concept ChangeCode0
Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors—0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
Aggression Identification and Multi Lingual Word Embeddings—0
Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE IdentificationCode0
Automatically Linking Lexical Resources with Word Sense Embedding Models—0
Show:102550
← PrevPage 258 of 401Next →

No leaderboard results yet.