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Language processing and learning models for community question answering in Arabic

机译：阿拉伯语社区问题解答的语言处理和学习模型

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In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA-from segmentation to constituency parsing-built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在本文中，我们重点讨论阿拉伯语社区问答（cQA）论坛中的问题排名问题。我们使用高级阿拉伯文本表示形式的机器学习算法来解决该任务。后者是通过将树核应用于带有文本相似性（包括词嵌入）的选区分析树而获得的。我们的两个主要贡献是：（i）基于UIMA的阿拉伯语处理管道-从分段到选区解析，建立在最先进的阿拉伯语处理工具包Farasa之上； （ii）应用长短期记忆神经网络来确定问题中的最佳文本片段，以用于我们基于树核的排名器中。我们对最近发布的cQA数据集进行的全面实验表明，Farasa提供的阿拉伯语语言处理产生了出色的结果，并且神经网络与树核相结合进一步提高了效率和准确性。正如我们在Farasa和Stanford解析器上所做的测试所证明的那样，我们的方法还可以在不同的处理管道之间进行隐式比较。 （C）2017 Elsevier Ltd.保留所有权利。

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