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 1411–1420 of 4002 papers

TitleStatusHype
Chinese Zero Pronoun Resolution with Deep Neural Networks—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
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts—0
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings—0
Evaluating vector-space models of analogy—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Show:102550
← PrevPage 142 of 401Next →

No leaderboard results yet.