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Classifying Bias in Large Multilingual Corpora via Crowdsourcing and Topic Modeling
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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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Citation Handling: Processing Citation Texts in Scientific Documents
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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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Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
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Abstract:
We describe a paradigm for combining manual and automatic error correction of noisy structured lexicographic data. Modifications to the structure and underlying text of the lexicographic data are expressed in a simple, interpreted programming language. Dictionary Manipulation Language (DML) commands identify nodes by unique identifiers, and manipulations are performed using simple commands such as create, move, set text, etc. Corrected lexicons are produced by applying sequences of DML commands to the source version of the lexicon. DML commands can be written manually to repair one-off errors or generated automatically to correct recurring problems. We discuss advantages of the paradigm for the task of editing digital bilingual dictionaries. ; 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:
computational linguistics; computer science; digital bilingual dictionaries; digital lexicography; electronic lexicography; error correction; noisy structured data; XML
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URL: https://doi.org/10.13016/M2RP4W http://hdl.handle.net/1903/15577
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Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
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Citation Handling for Improved Summarization of Scientific Documents
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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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IDS OBELEX meta
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Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks
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Headline Generation for Written and Broadcast News
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In: DTIC (2005)
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Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation
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In: DTIC (2003)
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