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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

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Showing 751–775 of 4002 papers

TitleStatusHype
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—0
A Survey on Sentence Embedding Models Performance for Patent AnalysisCode0
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement—0
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Approach to Predicting News -- A Precise Multi-LSTM Network With BERTCode0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
From Hyperbolic Geometry Back to Word EmbeddingsCode0
A Survey on Word Meta-Embedding Learning—0
Towards Arabic Sentence Simplification via Classification and Generative ApproachesCode0
Multimodal Hate Speech Detection from Bengali Memes and TextsCode0
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge—0
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCode0
Word Embeddings Are Capable of Capturing Rhythmic Similarity of WordsCode0
Automatic Extraction of Nested Entities in Clinical Referrals in SpanishCode0
A Part-of-Speech Tagger for YiddishCode0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to ModelingCode0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Semantic properties of English nominal pluralization: Insights from word embeddings—0
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese CodexCode0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
The SAME score: Improved cosine based bias score for word embeddings—0
Probabilistic Embeddings with Laplacian Graph Priors—0
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