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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 1111–1120 of 4002 papers

TitleStatusHype
Diachronic Embeddings for People in the News—0
An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition—0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Distilled Wasserstein Learning for Word Embedding and Topic Modeling—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Diachronic degradation of language models: Insights from social media—0
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring—0
An Exploration of Word Embedding Initialization in Deep-Learning Tasks—0
D-Graph: AI-Assisted Design Concept Exploration Graph—0
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