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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 2721–2730 of 4002 papers

TitleStatusHype
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models—0
When Polysemy Matters: Modeling Semantic Categorization with Word Embeddings—0
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
When Word Embeddings Become Endangered—0
Where exactly does contextualization in a PLM happen?—0
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit—0
Which Evaluations Uncover Sense Representations that Actually Make Sense?—0
Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models—0
Whom to Learn From? Graph- vs. Text-based Word Embeddings—0
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
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