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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 1641–1650 of 4002 papers

TitleStatusHype
Gender Roles from Word Embeddings in a Century of Children’s Books—0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings—0
Generating Adequate Distractors for Multiple-Choice Questions—0
Compositional Fusion of Signals in Data Embedding—0
Key Phrase Extraction & Applause Prediction—0
BERT's Conceptual Cartography: Mapping the Landscapes of Meaning—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
Compound Embedding Features for Semi-supervised Learning—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
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