dc.contributor.authorLi, Shaohua
dc.contributor.authorZhu, Jun
dc.contributor.authorMiao, Chunyan
dc.date.accessioned2017-05-19T02:47:30Z
dc.date.available2017-05-19T02:47:30Z
dc.date.copyright2017
dc.date.issued2016
dc.identifier.citationLi, S., Zhu, J., & Miao, C. (2016). PSDVec: A toolbox for incremental and scalable word embedding. Neurocomputing, 237, 405-409.en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://hdl.handle.net/10220/42454
dc.description.abstractPSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities between the words. PSDVec implements a word embedding learning method based on a weighted low-rank positive semidefinite approximation. To scale up the learning process, we implement a blockwise online learning algorithm to learn the embeddings incrementally. This strategy greatly reduces the learning time of word embeddings on a large vocabulary, and can learn the embeddings of new words without re-learning the whole vocabulary. On 9 word similarity/analogy benchmark sets and 2 Natural Language Processing (NLP) tasks, PSDVec produces embeddings that has the best average performance among popular word embedding tools. PSDVec provides a new option for NLP practitioners.en_US
dc.format.extent12 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesNeurocomputingen_US
dc.rights© 2016 Elsevier B. V. This is the author created version of a work that has been peer reviewed and accepted for publication by Neurocomputing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [https://doi.org/10.1016/j.neucom.2016.05.093].en_US
dc.subjectWord embeddingen_US
dc.subjectMatrix factorizationen_US
dc.titlePSDVec: A toolbox for incremental and scalable word embeddingen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2016.05.093
dc.description.versionAccepted versionen_US
dc.contributor.organizationResearch Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.identifier.rims200777


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