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 3601–3610 of 4002 papers

TitleStatusHype
On the Role of Seed Lexicons in Learning Bilingual Word Embeddings—0
Semantics-Driven Recognition of Collocations Using Word Embeddings—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
Character-based Neural Machine Translation—0
Morphological Smoothing and Extrapolation of Word Embeddings—0
Neural Networks For Negation Scope Detection—0
Recognizing Salient Entities in Shopping Queries—0
Learning Word Meta-Embeddings—0
Jointly Learning to Embed and Predict with Multiple Languages—0
On Approximately Searching for Similar Word Embeddings—0
Show:102550
← PrevPage 361 of 401Next →

No leaderboard results yet.