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MoRS At SemEval-2017 Task 3: Easy To Use SVM In Ranking Tasks

机译:MoRS在SemEval-2017任务3:易于使用的SVM进行排名任务

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This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced 'Morse', which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.
机译:本文介绍了我们的系统,称为Mors(模块化排名系统),发音为“莫尔斯”,其参与Semeval-2017的任务3。我们使用Mors执行社区问题应答任务3,该任务3根据他们在回答线程中的问题时重新排序一系列评论。这是针对用户社区创建的大量问题。至于这项挑战,我们希望恢复简单,易于使用,以及我们认为在非专家人的手中的遗忘技术,可以在自己的数据集中重复使用。我们的一些技术包括文本的注释,检索每个评论的元数据,POS标记和命名实体识别等。这些给出了语法分析和语义测量的地方。最后,我们展示并讨论了我们的结果和我们的方法的背景,这是一个名为MoQA的更全面的制度的一部分。

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