Unsupervised word embeddings capture latent knowledge from materials science literature

Keywords

Computer scienceProperty (philosophy)Identification (biology)Data scienceSociology of scientific knowledgeKnowledge extractionPoint (geometry)Scientific literatureWord (group theory)Table (database)Natural language processingInformation retrievalArtificial intelligenceLinguisticsData miningEpistemology

Affiliated Institutions

Related Publications

Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data

Alexis Conneau , Douwe Kiela , Holger Schwenk +2 more

Many modern NLP systems rely on word embeddings, previously trained in an\nunsupervised manner on large corpora, as base features. Efforts to obtain\nembeddings for larger chunk...

2017 arXiv (Cornell University) 2038 citations

WordNet: An Electronic Lexical Database

Adam Kilgarriff , Christiane Fellbaum

Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and imp...

2000 Language 11654 citations

Graph Convolutional Networks for Text Classification

Liang Yao , Chengsheng Mao , Yuan Luo

Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolu...

2019 Proceedings of the AAAI Conference on... 1867 citations

Knowledge Discovery in Databases: An Attribute-Oriented Approach

Jiawei Han , Yandong Cai , Nick Cercone

Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been d...

1992 385 citations

From data properties to evidence

David Bell

The problem of making decisions among propositions based on both uncertain data items and arguments which are not certain is addressed. The primary knowledge discovery issue add...

1993 IEEE Transactions on Knowledge and Da... 32 citations

Publication Info

Year: 2019
Type: article
Volume: 571
Issue: 7763
Pages: 95-98
Citations: 988
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Unsupervised word embeddings capture latent knowledge from materials science literature

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

988

OpenAlex

Cite This

APA Style

                            
                                
                                    Vahe Tshitoyan, 
                                
                                    John Dagdelen, 
                                
                                    Leigh Weston
                                
                                et al.
                            
                            (2019). 
                            Unsupervised word embeddings capture latent knowledge from materials science literature. 
                            Nature
                            , 571
                            (7763)
                            , 95-98.
                            https://doi.org/10.1038/s41586-019-1335-8
                        

Identifiers

DOI: 10.1038/s41586-019-1335-8