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A mixed-method approach to normalising Dr. Math microtext

机译：数学MicroText博士判定的混合方法方法

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摘要

Dr. Math is a South African math tutoring service, which allows high school learners to use a mobile phone-based chat application to contact volunteer tutors. The learners structure their queries in a form of microtext, which the tutors find difficult to interpret. This study presents an automated process as a means to normalise a Dr. Math microtext-based learner query, so that it more closely resembles English. The process consists of a variety of replacements, based on known short forms, pre-generated English-variants and phonetic similarity. Input queries are processed at a word-level, regardless of underlying grammar. The normalisation process is validated in two steps. The first step uses spell-checking to determine that the process improves the overall spelling accuracy, of the original microtext-based input queries, in both test and validation conditions. Krippendorff's α is used in the second validation step to calculate the level of agreement, with regards to the legibility of normalised text, between two human coders. The α value of 0.681 is deemed sufficient for exploratory research. The coders agree that the normalisation process yields an improvement in legibility.
机译：数学博士是南非数学辅导服务，允许高中学习者使用基于手机的聊天应用程序联系志愿者导师。学习者以MicroText形式构建他们的查询，其中辅导员难以解释。本研究提出了一种自动化过程，作为归一化基于数学MicroText的学习者查询的方法，使其更加与英语相似。该过程包括各种替代品，基于已知的短语，预先生成的英语 - 变体和语音相似性。无论底层语法如何，输入查询都在单词级别处理。标准化过程分两步验证。第一步使用拼写检查来确定该过程在测试和验证条件中提高了基于MicroText的输入查询的整体拼写精度。 Krippendorff的α在第二次验证步骤中用于计算协议水平，关于标准化文本的易读性，在两个人的编码器之间。 α值为0.681是足以进行探索性研究的。编码员同意正常化过程会产生有易读性的改善。

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