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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 1051–1100 of 4002 papers

TitleStatusHype
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings—0
OntoZSL: Ontology-enhanced Zero-shot LearningCode1
Content-Aware Speaker Embeddings for Speaker Diarisation—0
A study of text representations in Hate Speech DetectionCode0
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds—0
A Note on Argumentative Topology: Circularity and Syllogisms as Unsolved Problems—0
Bootstrapping Multilingual AMR with Contextual Word Alignments—0
Focusing Knowledge-based Graph Argument Mining via Topic Modeling—0
Using Word Embeddings to Uncover Discourses—0
Short Text Clustering with Transformers—0
A Neural Few-Shot Text Classification Reality CheckCode1
RelWalk A Latent Variable Model Approach to Knowledge Graph EmbeddingCode0
PolyLM: Learning about Polysemy through Language ModelingCode1
A Simple Disaster-Related Knowledge Base for Intelligent Agents—0
Debiasing Pre-trained Contextualised EmbeddingsCode1
Dictionary-based Debiasing of Pre-trained Word EmbeddingsCode0
Censorship of Online Encyclopedias: Implications for NLP Models—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
Artificial intelligence prediction of stock prices using social media—0
Enhanced word embeddings using multi-semantic representation through lexical chainsCode0
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual RetrievalCode0
Multi-sense embeddings through a word sense disambiguation processCode0
Word Alignment by Fine-tuning Embeddings on Parallel CorporaCode1
Can a Fruit Fly Learn Word Embeddings?Code1
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
Graph-of-Tweets: A Graph Merging Approach to Sub-event IdentificationCode0
Misspelling Correction with Pre-trained Contextual Language Model—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Political Depolarization of News Articles Using Attribute-aware Word Embeddings—0
Lex-BERT: Enhancing BERT based NER with lexicons—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Visual-Textual Attentive Semantic Consistency for Medical Report Generation—0
Kernel Methods in Hyperbolic Spaces—0
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
Key Phrase Extraction & Applause Prediction—0
WARP: Word-level Adversarial ReProgrammingCode1
Faster Training of Word Embeddings—0
Ruminating Word Representations with Random Noise Masking—0
Topic-aware Contextualized Transformers—0
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
Shortformer: Better Language Modeling using Shorter InputsCode1
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
Introducing Orthogonal Constraint in Structural ProbesCode0
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