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 1831–1840 of 4002 papers

TitleStatusHype
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Improving Interpretability of Word Embeddings by Generating Definition and Usage—0
Experiments on a Guarani Corpus of News and Social Media—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Show:102550
← PrevPage 184 of 401Next →

No leaderboard results yet.