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 2161–2170 of 4002 papers

TitleStatusHype
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages—0
Evaluating a Multi-sense Definition Generation Model for Multiple Languages—0
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus—0
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks—0
Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text—0
Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF—0
Evaluating Machine Common Sense via Cloze Testing—0
Evaluating Metrics for Bias in Word Embeddings—0
Show:102550
← PrevPage 217 of 401Next →

No leaderboard results yet.