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 1981–1990 of 4002 papers

TitleStatusHype
Japanese Lexical Simplification for Non-Native Speakers—0
Japanese Word Readability Assessment using Word Embeddings—0
JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
JeuxDeLiens: Word Embeddings and Path-Based Similarity for Entity Linking using the French JeuxDeMots Lexical Semantic Network—0
JHU System Description for the MADAR Arabic Dialect Identification Shared Task—0
Job Prediction: From Deep Neural Network Models to Applications—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
Dependency-Based Word Embeddings—0
Show:102550
← PrevPage 199 of 401Next →

No leaderboard results yet.