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 1931–1940 of 4002 papers

TitleStatusHype
Interpretable Neural Embeddings with Sparse Self-Representation—0
Event Detection Using Frame-Semantic Parser—0
Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings—0
Interpretable Word Embeddings via Informative Priors—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Evaluation of Word Embeddings for the Social Sciences—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Show:102550
← PrevPage 194 of 401Next →

No leaderboard results yet.