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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 926–950 of 4002 papers

TitleStatusHype
Hate and Offensive Speech Detection in Hindi and Marathi—0
Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language DataCode0
Inter-Sense: An Investigation of Sensory Blending in Fiction—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi—0
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models—0
Large Scale Substitution-based Word Sense Induction—0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
BI-RADS BERT & Using Section Segmentation to Understand Radiology ReportsCode0
Regionalized models for Spanish language variations based on Twitter—0
Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities—0
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference ResolutionCode0
Using Word Embeddings for Italian Crime News Categorization—0
Human-in-the-Loop Refinement of Word Embeddings—0
A Survey On Neural Word Embeddings—0
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy—0
A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings—0
Keyword-centered Collocating Topic Analysis—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features—0
Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases—0
Multi-granular Legal Topic Classification on Greek LegislationCode0
EDGAR-CORPUS: Billions of Tokens Make The World Go Round—0
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING—0
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