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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 1551–1560 of 4002 papers

TitleStatusHype
Fast query-by-example speech search using separable model—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
Detecting Cybersecurity Events from Noisy Short Text—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition—0
Feature Embedding for Dependency Parsing—0
Feature Engineering vs BERT on Twitter Data—0
A New Approach to Animacy Detection—0
Self-supervised audio representation learning for mobile devices—0
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