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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 1201–1225 of 4002 papers

TitleStatusHype
Censorship of Online Encyclopedias: Implications for NLP Models—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
Enhanced word embeddings using multi-semantic representation through lexical chainsCode0
Multi-sense embeddings through a word sense disambiguation processCode0
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual RetrievalCode0
Hostility Detection and Covid-19 Fake News Detection in Social Media—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of TwitterCode0
Misspelling Correction with Pre-trained Contextual Language Model—0
Graph-of-Tweets: A Graph Merging Approach to Sub-event IdentificationCode0
Political Depolarization of News Articles Using Attribute-aware Word Embeddings—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Lex-BERT: Enhancing BERT based NER with lexicons—0
Faster Training of Word Embeddings—0
Key Phrase Extraction & Applause Prediction—0
Topic-aware Contextualized Transformers—0
Visual-Textual Attentive Semantic Consistency for Medical Report Generation—0
Kernel Methods in Hyperbolic Spaces—0
Ruminating Word Representations with Random Noise Masking—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs—0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings—0
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