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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 2451–2460 of 4002 papers

TitleStatusHype
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision—0
Topological Data Analysis for Word Sense Disambiguation—0
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
Topology of Word Embeddings: Singularities Reflect Polysemy—0
Towards Automated Website Classification by Deep Learning—0
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings—0
Toward Better Storylines with Sentence-Level Language Models—0
Toward Incorporation of Relevant Documents in word2vec—0
Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions—0
Toward Mention Detection Robustness with Recurrent Neural Networks—0
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