Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling

Simple item page
dc.contributor.authorRodrigues, Paul
dc.contributor.authorZajic, David
dc.contributor.authorDoermann, David
dc.contributor.authorBloodgood, Michael
dc.contributor.authorYe, Peng
dc.date.accessioned2014-08-20T21:25:25Z
dc.date.available2014-08-20T21:25:25Z
dc.date.issued2011-11
dc.description.abstractDictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent lexical entries, and utilize descendants of these repeating elements to represent the structure within each lexical entry, in the form of an XML tree. In many cases, dictionaries are published that have errors and inconsistencies that are expensive to find manually. This paper discusses a method for dictionary writers to quickly audit structural regularity across entries in a dictionary by using statistical language modeling. The approach learns the patterns of XML nodes that could occur within an XML tree, and then calculates the probability of each XML tree in the dictionary against these patterns to look for entries that diverge from the norm.en_US
dc.description.sponsorshipThis material is based upon work supported, in whole or in part, with funding from the United States Government. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the University of Maryland, College Park and/or any agency or entity of the United States Government. Nothing in this report is intended to be and shall not be treated or construed as an endorsement or recommendation by the University of Maryland, United States Government, or the authors of the product, process, or service that is the subject of this report. No one may use any information contained or based on this report in advertisements or promotional materials related to any company product, process, or service or in support of other commercial purposes.en_US
dc.identifierhttps://doi.org/10.13016/M2WC75
dc.identifier.citationPaul Rodrigues, David Zajic, David Doermann, Michael Bloodgood, and Peng Ye. 2011. Detecting structural irregularity in electronic dictionaries using language modeling. In Proceedings of Electronic Lexicography in the 21st Century (eLex), pages 227-232, Bled, Slovenia, November. Trojina Institute for Applied Slovene Studies.en_US
dc.identifier.urihttp://hdl.handle.net/1903/15576
dc.language.isoen_USen_US
dc.publisherTrojina Institute for Applied Slovene Studiesen_US
dc.relation.isAvailableAtCenter for Advanced Study of Language
dc.relation.isAvailableAtDigitial Repository at the University of Maryland
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md)
dc.subjectcomputer scienceen_US
dc.subjectstatistical methodsen_US
dc.subjectcomputational linguisticsen_US
dc.subjectnatural language processingen_US
dc.subjecthuman language technologyen_US
dc.subjectelectronic lexicographyen_US
dc.subjectXMLen_US
dc.subjectlanguage modelingen_US
dc.subjectanomaly detectionen_US
dc.subjecterror correctionen_US
dc.subjectelectronic dictionariesen_US
dc.titleDetecting Structural Irregularity in Electronic Dictionaries Using Language Modelingen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
detectingStructuralIrregularity_eLex2011.pdf
Size:
1.14 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.57 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Center for Advanced Study of Language Research Works