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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 1461–1470 of 4002 papers

TitleStatusHype
Example-based Acquisition of Fine-grained Collocation Resources—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Experiments on a Guarani Corpus of News and Social Media—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
Detecting Fake News with Capsule Neural Networks—0
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