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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 1201–1225 of 4002 papers

TitleStatusHype
Document-Level Machine Translation with Word Vector Models—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Do gender neutral affixes naturally reduce gender bias in static word embeddings?—0
Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words—0
Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings—0
Domain adaptation for part-of-speech tagging of noisy user-generated text—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval—0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences—0
An exploration of the encoding of grammatical gender in word embeddings—0
Do Not Harm Protected Groups in Debiasing Language Representation Models—0
Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings—0
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Development of a Japanese Personality Dictionary based on Psychological Methods—0
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects—0
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection—0
Developing Conversational Data and Detection of Conversational Humor in Telugu—0
Beyond Context: A New Perspective for Word Embeddings—0
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