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 2861–2870 of 4002 papers

TitleStatusHype
Membership Inference on Word Embedding and Beyond—0
Merging Verb Senses of Hindi WordNet using Word Embeddings—0
Meta-Embeddings for Natural Language Inference and Semantic Similarity tasks—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German—0
Metaphor Detection in a Poetry Corpus—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Metaphor Detection using Deep Contextualized Word Embeddings—0
Methodical Evaluation of Arabic Word Embeddings—0
Methods for Numeracy-Preserving Word Embeddings—0
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification—0
Show:102550
← PrevPage 287 of 401Next →

No leaderboard results yet.