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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 1201–1250 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
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text—0
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
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs—0
Key Phrase Extraction & Applause Prediction—0
Ruminating Word Representations with Random Noise Masking—0
Kernel Methods in Hyperbolic Spaces—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Topic-aware Contextualized Transformers—0
Visual-Textual Attentive Semantic Consistency for Medical Report Generation—0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings—0
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Introducing Orthogonal Constraint in Structural ProbesCode0
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings—0
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali LanguageCode0
WEmbSim: A Simple yet Effective Metric for Image Captioning—0
Improved Biomedical Word Embeddings in the Transformer EraCode0
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings—0
Intrinsic Image Captioning Evaluation—0
A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings—0
Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery—0
TF-CR: Weighting Embeddings for Text ClassificationCode0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings—0
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity SearchCode0
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic ChangeCode0
A Computational Approach to Measuring the Semantic Divergence of Cognates—0
Automatic Word Association Norms (AWAN)—0
“Shakespeare in the Vectorian Age” – An evaluation of different word embeddings and NLP parameters for the detection of Shakespeare quotes—0
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings—0
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation TaskCode0
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings—0
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora—0
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