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 591–600 of 4002 papers

TitleStatusHype
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
A Lexicalized Tree Kernel for Open Information Extraction—0
A Call for More Rigor in Unsupervised Cross-lingual Learning—0
Case Studies on using Natural Language Processing Techniques in Customer Relationship Management Software—0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings—0
Regionalized models for Spanish language variations based on Twitter—0
Show:102550
← PrevPage 60 of 401Next →

No leaderboard results yet.