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 3501–3510 of 4002 papers

TitleStatusHype
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank—0
Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels—0
Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment—0
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction—0
Cross-lingual hate speech detection based on multilingual domain-specific word embeddings—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Cross-lingual Linking of Automatically Constructed Frames and FrameNet—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0—0
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Show:102550
← PrevPage 351 of 401Next →

No leaderboard results yet.