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dc.contributor.authorQasemiZadeh, Behrang
dc.contributor.authorHandschuh, Siegfried
dc.date.accessioned2014-10-21T09:18:01Z
dc.date.available2014-10-21T09:18:01Z
dc.date.issued2014
dc.identifier.citationQasemiZadeh, Behrang; Handschuh, Siegfried (2014) Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction Empirical Methods in Natural Language Processing (EMNLP) Doha, Qatar, 2014-10-25- 2014-10-29en_US
dc.identifier.urihttp://hdl.handle.net/10379/4650
dc.description.abstractVector space models (VSMs) are mathematically well-defined frameworks that have been widely used in the distributional approaches to semantics. In VSMs, high-dimensional vectors represent linguistic entities. In an application, the similarity of vectors and thus the entities that they represent is computed by a distance formula. The high dimensionality of vectors, however, is a barrier to the performance of methods that employ VSMs. Consequently, a dimensionality reduction technique is employed to alleviate this problem. This paper introduces a novel technique called Random Manhattan Indexing (RMI) for the construction of L1 normed VSMs at reduced dimensionality. RMI combines the construction of a VSM and dimension reduction into an incremental and thus scalable two-step procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections. We further introduce Random Manhattan Integer Indexing (RMII): a computationally enhanced version of RMI. As shown in the reported experiments, RMI and RMII can be used reliably to estimate the L1 distances between vectors in a vector space of low dimensionality.en_US
dc.language.isoenen_US
dc.relation.ispartofEmpirical Methods in Natural Language Processing (EMNLP)en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectRandom Manhattan Integer Indexingen_US
dc.subjectRandom Indexingen_US
dc.subjectRandom Projectionen_US
dc.subjectCity Block distanceen_US
dc.subjectL1 normed vector spaceen_US
dc.subjectDistributional semantic modelsen_US
dc.subjectvector space modelsen_US
dc.subjectnatural language processingen_US
dc.titleRandom Manhattan Integer Indexing: Incremental L1 Normed Vector Space Constructionen_US
dc.typeConference Paperen_US
dc.date.updated2014-10-20T17:52:45Z
dc.local.publishedsourcehttp://emnlp2014.org/papers/pdf/EMNLP2014178.pdfen_US
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|SFI|~|
dc.internal.rssid7474328
dc.local.contactBehrang Qasemizadeh, Deri, Ida Business Park, Lower Dangan, Nui Galway. Email: behrang.qasemizadeh@deri.org
dc.local.copyrightcheckedYes
dc.local.versionPUBLISHED
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland