An In-depth Analysis of the Effect of Text Normalization in Social Media
An In-depth Analysis of the Effect of Text Normalization in Social Media
复制标题
深入分析社交媒体中文本规范化的效果
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Yunyao Li
中科院分区：
文献类型：
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作者：
Tyler Baldwin;Yunyao Li
近年来，人们对社交媒体中的文本规范化越来越感兴趣，因为Twitter和其他社交媒体数据中的非正式写作风格经常给NLP应用程序带来问题。不幸的是，目前的大多数方法都狭隘地将标准化任务视为一项“一刀切”的任务，即用标准单词替换非标准单词。在这项工作中，我们建立了归一化编辑的分类，并提出了归一化研究，以检查其对三个不同的下游应用(依存关系解析、命名实体识别和文本到语音合成)的影响。结果表明，应该如何看待标准化任务高度依赖于目标应用程序。结果还表明，标准化必须被认为不仅仅是单词替换，才能产生与干净文本上看到的结果相当的结果。
Recent years have seen increased interest in text normalization in social media, as the informal writing styles found in Twitter and other social media data often cause problems for NLP applications. Unfortunately, most current approaches narrowly regard the normalization task as a “one size fits all” task of replacing non-standard words with their standard counterparts. In this work we build a taxonomy of normalization edits and present a study of normalization to examine its effect on three different downstream applications (dependency parsing, named entity recognition, and text-to-speech synthesis). The results suggest that how the normalization task should be viewed is highly dependent on the targeted application. The results also show that normalization must be thought of as more than word replacement in order to produce results comparable to those seen on clean text.
回复：“使用数值方法设计模拟：重新审视平衡截距”。
影响因子：
5
作者：
Zivich,PaulN;Ross,RachaelK
通讯作者：
Ross,RachaelK