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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 1651–1660 of 4002 papers

TitleStatusHype
On the Reliability of Test Collections for Evaluating Systems of Different Types—0
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space—0
Intuitive Contrasting Map for Antonym EmbeddingsCode0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?Code0
Upgrading the Newsroom: An Automated Image Selection System for News Articles—0
On Adversarial Examples for Biomedical NLP Tasks—0
Revisiting the Context Window for Cross-lingual Word Embeddings—0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT—0
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