We consider entity-level sentiment analysis in Arabic, a morphologically rich
language with increasing resources. We present a system that is applied to
complex posts written in response to Arabic newspaper articles. Our goal is to
identify important entity "targets" within the post along with the polarity
expressed about each target. We achieve significant improvements over multiple
baselines, demonstrating that the use of specific morphological representations
improves the performance of identifying both important targets and their
sentiment, and that the use of distributional semantic clusters further boosts
performances for these representations, especially when richer linguistic
resources are not available.

本文通过使用特定的形态学表示和分布式语义聚类等技术，对阿拉伯语中的实体级情感分析进行了改良，显著提高了实体目标以及其情感的识别精度，特别是当丰富的语言资源不可用时。

SMARTies: 针对阿拉伯目标实体的情感模型

SMARTies: Sentiment Models for Arabic Target Entities

This paper presents a scalable method for integrating compositional
morphological representations into a vector-based probabilistic language model.
Our approach is evaluated in the context of log-bilinear language models,
rendered suitably efficient for implementation inside a machine translation
decoder by factoring the vocabulary. We perform both intrinsic and extrinsic
evaluations, presenting results on a range of languages which demonstrate that
our model learns morphological representations that both perform well on word
similarity tasks and lead to substantial reductions in perplexity. When used
for translation into morphologically rich languages with large vocabularies,
our models obtain improvements of up to 1.2 BLEU points relative to a baseline
system using back-off n-gram models.

本论文提出了一种可扩展的方法，将组成形态学表示集成到基于向量的概率语言模型中，并在语言模型因词汇而被适当地实现以实现内部和外部评估，在一些语言上进行实验研究并展示结果，表明我们的模型学习了形态学表示，在词相似性任务上表现出色且使困惑度大幅降低，用于转化大词汇量的形态丰富语言时，我们的模型相对于使用回退 N-gram 模型的基线系统，可以获得高达 1.2 的蓝点改进。