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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 3931–3940 of 4002 papers

TitleStatusHype
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
Extremely Small BERT Models from Mixed-Vocabulary Training—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook—0
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users—0
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