Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction
Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction
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DOI：
10.1145/3534678.3539386
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Mohna Chakraborty;Adithya Kulkarni;Qi Li
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文献类型：
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作者：
Mohna Chakraborty;Adithya Kulkarni;Qi Li
方面意见提取任务从评论中提取方面术语和意见术语。监督提取方法实现了最先进的性能，但需要大规模的人工注释训练数据。因此，由于缺乏训练数据，它们被限制用于开放域任务。这项工作解决了这一挑战，同时地雷方面的条款，意见条款，以及他们的对应关系，在一个联合模型。提出了一种基于双层跨度抽取框架的开放域意见-观点联合挖掘（ODAO）方法。ODAO不需要人工标注，而是采用基于规则的通用依赖分析为未标注语料生成弱标注。然后，ODAO利用这种弱监督来训练双层跨度提取框架，以提取方面术语（ATE），意见术语（OTE）和方面意见对（AOPE）。ODAO采用典型相关分析作为早期停止指标，避免模型对噪声的过度拟合，以解决噪声弱监管问题。ODAO采用自训练过程来逐步丰富训练数据，以解决弱监管偏差问题。我们进行了广泛的实验，并证明了拟议的ODAO的力量。在四个基准数据集上进行的方面-意见协同提取和对提取任务的结果表明，与最先进的全监督方法相比，ODAO可以实现竞争力甚至更好的性能。
The aspect-opinion extraction tasks extract aspect terms and opinion terms from reviews. The supervised extraction methods achieve state-of-the-art performance but require large-scale human-annotated training data. Thus, they are restricted for open-domain tasks due to the lack of training data. This work addresses this challenge and simultaneously mines aspect terms, opinion terms, and their correspondence in a joint model. We propose an Open-Domain Aspect-Opinion Co-Mining (ODAO) method with a Double-Layer span extraction framework. Instead of acquiring human annotations, ODAO first generates weak labels for unannotated corpus by employing rules-based on universal dependency parsing. Then, ODAO utilizes this weak supervision to train a double-layer span extraction framework to extract aspect terms (ATE), opinion terms (OTE), and aspect-opinion pairs (AOPE). ODAO applies canonical correlation analysis as an early stopping indicator to avoid the model over-fitting to the noise to tackle the noisy weak supervision. ODAO applies a self-training process to gradually enrich the training data to tackle the weak supervision bias issue. We conduct extensive experiments and demonstrate the power of the proposed ODAO. The results on four benchmark datasets for aspect-opinion co-extraction and pair extraction tasks show that ODAO can achieve competitive or even better performance compared with the state-of-the-art fully supervised methods.