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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 1621–1630 of 4002 papers

TitleStatusHype
Abusive language in Spanish children and young teenager's conversations: data preparation and short text classification with contextual word embeddings—0
Building Sense Representations in Danish by Combining Word Embeddings with Lexical Resources—0
Translating Knowledge Representations with Monolingual Word Embeddings: the Case of a Thesaurus on Corporate Non-Financial Reporting—0
Building Semantic Grams of Human Knowledge—0
Embedding Space Correlation as a Measure of Domain Similarity—0
BUCC2020: Bilingual Dictionary Induction using Cross-lingual Embedding—0
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature—0
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study—0
``A Passage to India'': Pre-trained Word Embeddings for Indian Languages—0
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu—0
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