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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 951–975 of 4002 papers

TitleStatusHype
Variational Gaussian Topic Model with Invertible Neural Projections—0
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media—0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning—0
TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial StudyCode0
An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource—0
WOVe: Incorporating Word Order in GloVe Word Embeddings—0
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Coming to its senses: Lessons learned from Approximating Retrofitted BERT representations for Word Sense information—0
Empirical Analysis of Image Caption Generation using Deep Learning—0
Playing Codenames with Language Graphs and Word EmbeddingsCode1
Multilingual Offensive Language Identification for Low-resource Languages—0
Evaluation Of Word Embeddings From Large-Scale French Web ContentCode0
Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social MediaCode0
Large-scale Taxonomy Induction Using Entity and Word Embeddings—0
GraphTMT: Unsupervised Graph-based Topic Modeling from Video TranscriptsCode0
Impact of Gender Debiased Word Embeddings in Language Modeling—0
Learning to Lemmatize in the Word Representation Space—0
Cross-lingual hate speech detection based on multilingual domain-specific word embeddings—0
Mitigating Political Bias in Language Models Through Reinforced Calibration—0
A Bi-Encoder LSTM Model For Learning Unstructured DialogsCode0
A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP—0
Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word EmbeddingsCode0
Deep Clustering with Measure Propagation—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
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