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Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation ...
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Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ...
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A random forest system combination approach for error detection in digital dictionaries ...
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Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
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A random forest system combination approach for error detection in digital dictionaries
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Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ...
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Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
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Abstract:
Dictionaries 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. ... : This 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. ...
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Keyword:
anomaly detection; computational linguistics; computer science; electronic dictionaries; electronic lexicography; error correction; human language technology; language modeling; natural language processing; statistical methods; XML
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URL: http://hdl.handle.net/1903/15576 https://dx.doi.org/10.13016/m2wc75
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Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
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Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
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Error Correction for Arabic Dictionary Lookup
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In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), Valetta, 17 - 23 May 2010 (2010), 263-268
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Unsupervised Morphology Induction for Part-of-speech-tagging
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In: University of Pennsylvania Working Papers in Linguistics (2006)
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