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 2301–2310 of 4002 papers

TitleStatusHype
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Field Embedding: A Unified Grain-Based Framework for Word Representation—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation—0
Finding Individual Word Sense Changes and their Delay in Appearance—0
Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Show:102550
← PrevPage 231 of 401Next →

No leaderboard results yet.