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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 1511–1520 of 4002 papers

TitleStatusHype
Coming to its senses: Lessons learned from Approximating Retrofitted BERT representations for Word Sense information—0
A Simple Word Embedding Model for Lexical Substitution—0
Amrita\_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension—0
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings—0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
A Simple Language Model based on PMI Matrix Approximations—0
Amplifying the Range of News Stories with Creativity: Methods and their Evaluation, in Portuguese—0
Addressing the Challenges of Cross-Lingual Hate Speech Detection—0
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