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首页> 外文期刊>ACM transactions on Asian language information processing >AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language
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AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language

机译：AROMA：用于阿拉伯语作为低资源语言的意见挖掘的递归深度学习模型

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摘要

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A Recursive Deep Learning Model for Opinion Mining in Arabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.
机译：尽管英语民意挖掘的研究已经取得了巨大的进展和成功，但阿拉伯民意挖掘的工作仍然落后。这主要是由于在开发用于阿拉伯语的自然语言处理（NLP）方法，处理其形态复杂性方面的研究工作相对较新，以及缺乏用于阿拉伯语的大规模民意资源。为了弥补这一差距，我们检查了英语使用的模型类别，这些模型不需要大量使用NLP或意见资源。特别是，我们考虑了递归自动编码器（RAE）。但是，由于RAE模型在处理阿拉伯语的形态复杂性，为自动编码器提供更完整和全面的输入功能以及遵循自然方式成分进行语义合成方面存在局限性，因此在阿拉伯语中不如在英语中获得成功结合起来表达整体含义。在本文中，我们提出了一种阿拉伯语意见挖掘的递归深度学习模型（AROMA），它解决了这些限制。在代表不同体裁和写作风格的三种阿拉伯语料库上对AROMA进行了评估。结果表明，与基准RAE相比，AROMA的性能有了显着提高。它也优于文献中的几种著名方法。

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