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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 471–480 of 4002 papers

TitleStatusHype
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings—0
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks—0
Automated Discovery of Mathematical Definitions in Text—0
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks—0
Automated essay scoring with string kernels and word embeddings—0
An Analysis of Hierarchical Text Classification Using Word Embeddings—0
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition—0
BERTMap: A BERT-based Ontology Alignment System—0
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