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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 2271–2280 of 4002 papers

TitleStatusHype
Extracting domain-specific terms using contextual word embeddings—0
Extracting Possessions from Social Media: Images Complement Language—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
Extracting Temporal and Causal Relations between Events—0
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Extractive Summarization using Continuous Vector Space Models—0
Extrapolating Binder Style Word Embeddings to New Words—0
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference—0
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