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 1501–1510 of 4002 papers

TitleStatusHype
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation—0
On the Correlation of Word Embedding Evaluation Metrics—0
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection TaskCode1
Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks—0
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping—0
Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation—0
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?—0
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?—0
Offensive Language Detection Using Brown Clustering—0
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies—0
Show:102550
← PrevPage 151 of 401Next →

No leaderboard results yet.