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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 1481–1490 of 4002 papers

TitleStatusHype
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine—0
ASR error management for improving spoken language understanding—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach—0
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection—0
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