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 3541–3550 of 4002 papers

TitleStatusHype
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Redefining part-of-speech classes with distributional semantic models—0
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval—0
Morphological Priors for Probabilistic Neural Word Embeddings—0
New word analogy corpus for exploring embeddings of Czech wordsCode0
Exponential Family Embeddings—0
How to Train good Word Embeddings for Biomedical NLPCode0
Show:102550
← PrevPage 355 of 401Next →

No leaderboard results yet.