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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 526–550 of 4002 papers

TitleStatusHype
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional NetworksCode0
Toward Understanding Bias Correlations for Mitigation in NLP—0
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word AlignmentCode0
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?—0
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic—0
Recovering Private Text in Federated Learning of Language ModelsCode1
Disentangling Visual Embeddings for Attributes and ObjectsCode1
What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris—0
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic RepresentationsCode1
Design and Implementation of a Quantum Kernel for Natural Language ProcessingCode0
Vision Transformer: Vit and its Derivatives—0
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman ProblemCode1
Hyperbolic Relevance Matching for Neural Keyphrase ExtractionCode1
Using virtual edges to extract keywords from texts modeled as complex networks—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
The Limits of Word Level Differential Privacy—0
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource LanguageCode0
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions—0
LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration MemoryCode0
DLRG@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil using Multilingual Transformer Models—0
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation—0
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model—0
Binary Encoded Word Mover’s Distance—0
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