用于清除脏文本的增强综合评分
Enhanced Integrated Scoring for Cleaning Dirty Texts
摘要：
越来越多的本体工程的方法，从文本是面向使用在线资源，如公司内部网和万维网。尽管有这样的上升，没有太多的工作可以找到在预处理和清理脏文本从在线源方面。本文提出了一种改进的综合评分拼写错误纠正，缩写扩展和情况恢复（ISSAC）。ISSAC是本体工程系统中文本预处理阶段的一部分。使用700条聊天记录对增强的ISSAC进行的新评估显示，与仅使用基本ISSAC和Aspell的96.5%和71%相比，准确率提高了98%。
英文摘要：
An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
