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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 571–580 of 4002 papers

TitleStatusHype
A Process for Topic Modelling Via Word Embeddings—0
ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora—0
BUCC2020: Bilingual Dictionary Induction using Cross-lingual Embedding—0
A Probabilistic Model for Learning Multi-Prototype Word Embeddings—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning—0
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings—0
A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work—0
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective—0
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation—0
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