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Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)最新文献

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Mining Lexical Variants from Microblogs: An Unsupervised Multilingual Approach 从微博中挖掘词汇变体:一种无监督的多语言方法
Alejandro Mosquera, Paloma Moreda Pozo
User-generated content has become a recurrent resource for NLP tools and applications, hence many efforts have been made lately in order to handle the noise present in short social media texts. The use of normalisation techniques has been proven useful for identifying and replacing lexical variants on some of the most informal genres such as microblogs. But annotated data is needed in order to train and evaluate these systems, which usually involves a costly process. Until now, most of these approaches have been focused on English and they were not taking into account demographic variables such as the user location and gender. In this paper we describe the methodology used for automatically mining a corpus of variant and normalisation pairs from English and Spanish tweets.
用户生成的内容已经成为NLP工具和应用程序的经常性资源,因此最近已经做出了许多努力来处理短社交媒体文本中存在的噪音。规范化技术的使用已被证明对识别和替换一些最非正式的文体(如微博)上的词汇变体很有用。但是,为了训练和评估这些系统,需要带注释的数据,这通常涉及一个昂贵的过程。到目前为止,这些方法大多集中在英语上,没有考虑到用户位置和性别等人口统计变量。在本文中,我们描述了用于从英语和西班牙语推文中自动挖掘变体和规范化对的语料库的方法。
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Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)
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