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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model
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Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model

机译：基于词对齐模型的在线评论中共提取意见目标和意见词

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摘要

Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations. Compared to syntax-based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state-of-the-art methods.
机译：从在线评论中挖掘意见目标和意见词是细化意见挖掘的重要任务，其关键组成部分涉及检测词之间的意见关系。为此，本文提出了一种基于部分监督的对齐模型的新颖方法，该方法将识别意见关系视为对齐过程。然后，利用基于图的协同排序算法来估计每个候选者的置信度。最后，提取具有较高置信度的候选者作为意见目标或意见词。与以前的基于最近邻居规则的方法相比，我们的模型可以更精确地捕获意见关系，尤其是大跨度关系。与基于语法的方法相比，我们的单词对齐模型可以有效地缓解处理非正式在线文本时解析错误的负面影响。特别是，与传统的非监督对准模型相比，该模型由于使用了部分监督而获得了更高的精度。此外，在估计候选者置信度时，我们会在基于图的协同排序算法中对更高阶的顶点进行惩罚，以降低错误生成的可能性。我们对三种不同大小和语言的语料库的实验结果表明，我们的方法有效地胜过了最先进的方法。

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