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Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection

dc.contributor.authorBloodgood, Michael
dc.contributor.authorStrauss, Benjamin
dc.identifier.citationMichael Bloodgood and Benjamin Strauss. Data cleaning for XML electronic dictionaries via statistical anomaly detection. In Proceedings of the 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), pages 79-86, Laguna Hills, CA, USA, February 2016. IEEE.en_US
dc.identifier.otherDOI 10.1109/ICSC.2016.38
dc.description.abstractMany important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon’s Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.en_US
dc.subjectcomputer scienceen_US
dc.subjectstatistical methodsen_US
dc.subjectOptical Character Recognitionen_US
dc.subjectartificial intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectcomputational linguisticsen_US
dc.subjectnatural language processingen_US
dc.subjecthuman language technologyen_US
dc.subjecttext processingen_US
dc.subjectdata cleaningen_US
dc.subjectdata cleansingen_US
dc.subjectAmazon Mechanical Turken_US
dc.subjectelectronic lexicographyen_US
dc.subjectdigital dictionariesen_US
dc.subjectsemantic computingen_US
dc.subjectanomaly detectionen_US
dc.subjecterror detectionen_US
dc.titleData Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detectionen_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)

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