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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 341–350 of 4002 papers

TitleStatusHype
A Deep Learning Approach to Behavior-Based Learner Modeling—0
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
A Deep Learning approach for Hindi Named Entity Recognition—0
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models—0
A Multimodal Approach towards Emotion Recognition of Music using Audio and Lyrical Content—0
A multi-level approach for hierarchical Ticket Classification—0
A Deep Fusion Model for Domain Adaptation in Phrase-based MT—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
A multilabel approach to morphosyntactic probing—0
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