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 881–890 of 4002 papers

TitleStatusHype
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
BioReddit: Word Embeddings for User-Generated Biomedical NLP—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
Abstractive Document Summarization with Word Embedding Reconstruction—0
Context Vectors are Reflections of Word Vectors in Half the Dimensions—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation—0
Cross-lingual Transfer of Sentiment Classifiers—0
Show:102550
← PrevPage 89 of 401Next →

No leaderboard results yet.