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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 961–970 of 4002 papers

TitleStatusHype
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective DetectionCode0
Augmenting semantic lexicons using word embeddings and transfer learningCode0
Fast query-by-example speech search using separable model—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Gender Roles from Word Embeddings in a Century of Children’s Books—0
Task-adaptive Pre-training of Language Models with Word Embedding Regularization—0
Revisiting Tri-training of Dependency ParsersCode0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus—0
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme ClassificationCode0
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