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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 876–900 of 4002 papers

TitleStatusHype
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness—0
Contextualized moral inference—0
Contextualized Spoken Word Representations from Convolutional Autoencoders—0
Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge—0
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding—0
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
BioReddit: Word Embeddings for User-Generated Biomedical NLP—0
Context Vectors are Reflections of Word Vectors in Half the Dimensions—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation—0
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
Attending Sentences to detect Satirical Fake News—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Abstractive Document Summarization with Word Embedding Reconstruction—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
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