论文标题
通过Twitter玻璃：在微观文本中检测问题
Through the Twitter Glass: Detecting Questions in Micro-Text
论文作者
论文摘要
在另一项研究中，我们有兴趣了解Twitter上人们的问答习惯。在Twitter中找到问题是一个艰难的挑战，因此我们考虑将一些传统的NLP方法用于该问题。一方面，Twitter充满了特质，这使得处理困难。另一方面，它的长度非常限制，并且倾向于采用简单的句法结构，这可以帮助NLP处理的性能。为了找出NLP和Twitter的生存能力，我们建立了一条工具管道，以专门与Twitter输入一起使用，以便在推文中查找问题的任务。这项工作仍然是初步的，但是在本文中，我们讨论了我们使用的技术以及我们学到的教训。
In a separate study, we were interested in understanding people's Q&A habits on Twitter. Finding questions within Twitter turned out to be a difficult challenge, so we considered applying some traditional NLP approaches to the problem. On the one hand, Twitter is full of idiosyncrasies, which make processing it difficult. On the other, it is very restricted in length and tends to employ simple syntactic constructions, which could help the performance of NLP processing. In order to find out the viability of NLP and Twitter, we built a pipeline of tools to work specifically with Twitter input for the task of finding questions in tweets. This work is still preliminary, but in this paper we discuss the techniques we used and the lessons we learned.
