Show simple item record

dc.contributor.authorQasemiZadeh, Behrang
dc.contributor.authorBuitelaar, Paul
dc.contributor.authorMonaghan, Fergal
dc.date.accessioned2014-08-19T14:14:41Z
dc.date.available2014-08-19T14:14:41Z
dc.date.issued2010
dc.identifier.citationQasemizadeh, Behrang; Buitelaar, Paul; Monaghan, Fergal (2010) Developing a Dataset for Technology Structure Mining. Conference Paperen_US
dc.identifier.urihttp://www.deri.ie/sites/default/files/publications/bq_ieee_icsc_bare_conf.pdf
dc.identifier.urihttp://hdl.handle.net/10379/4514
dc.descriptionConference paperen_US
dc.description.abstractThis paper describes steps that have been taken to construct a development dataset for the task of Technology Structure Mining. We have defined the proposed task as the process of mapping a scientific corpus into a labeled digraph named a Technology Structure Graph as described in the paper. The generated graph expresses the domain semantics in terms of interdependencies between pairs of technologies that are named (introduced) in the target scientific corpus. The dataset comprises a set of sentences extracted from the ACL Anthology Corpus. Each sentence is annotated with at least two technologies in the domain of Human Language Technology and the interdependence between them. The annotations - technology mark-up and their interdependencies - are expressed at two layers: lexical and termino-conceptual. Lexical representation of technologies comprises varying lexicalizations of a technology. However, at the termino-conceptual layer all these lexical variations refer to the same concept. We have adopted the same approach for representing Semantic Relations, at the lexical layer a semantic relation is a predicate i.e. defined based on the sentence surface structure, however at the termino-conceptual layer semantic relations are classified into conceptual relations either taxonomic or non-taxonomic. Morover, the contexts that interdependencies are extracted from are classified into five groups based on the linguistic criteria and syntactic structure that are identified by the human annotators. The dataset initially comprises of 482 sentences. We hope this effort results in a benchmark that can be used for the technology structure mining task as defined in the paper.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectInformation Extractionen_US
dc.titleDeveloping a Dataset for Technology Structure Miningen_US
dc.typeConference Paperen_US
dc.date.updated2014-06-26T08:30:21Z
dc.identifier.doi10.1109/ICSC.2010.73
dc.local.publishedsourcehttp://dx.doi.org/10.1109/ICSC.2010.73en_US
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funder|~|SFI|~|
dc.internal.rssid6470237
dc.local.contactBehrang Qasemizadeh, Deri, Ida Business Park, Lower Dangan, Nui Galway. Email: behrang.qasemizadeh@deri.org
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
nui.item.downloads172


Files in this item

Attribution-NonCommercial-NoDerivs 3.0 Ireland
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

The following license files are associated with this item:

Thumbnail

This item appears in the following Collection(s)

Show simple item record