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 2171–2180 of 4002 papers

TitleStatusHype
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Evaluating Neural Word Representations in Tensor-Based Compositional Settings—0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
Evaluating Sub-word Embeddings in Cross-lingual Models—0
Evaluating the Consistency of Word Embeddings from Small Data—0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling—0
Evaluating the Stability of Embedding-based Word Similarities—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Show:102550
← PrevPage 218 of 401Next →

No leaderboard results yet.