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 2641–2650 of 4002 papers

TitleStatusHype
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures—0
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
LCT-MALTA's Submission to RepEval 2017 Shared Task—0
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning—0
LEAPME: Learning-based Property Matching with Embeddings—0
Learned In Speech Recognition: Contextual Acoustic Word Embeddings—0
Learned in Speech Recognition: Contextual Acoustic Word Embeddings—0
Learning acoustic word embeddings with phonetically associated triplet network—0
Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search—0
Show:102550
← PrevPage 265 of 401Next →

No leaderboard results yet.