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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 2071–2080 of 4002 papers

TitleStatusHype
EduBERT: Pretrained Deep Language Models for Learning Analytics—0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing—0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
Effect of dimensionality change on the bias of word embeddings—0
Effect of Text Color on Word Embeddings—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Effects of Creativity and Cluster Tightness on Short Text Clustering Performance—0
Effects of Word Embeddings on Neural Network-based Pitch Accent Detection—0
Efficient Contextual Representation Learning Without Softmax Layer—0
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