论文标题
开放域建议采矿利用细粒分析
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis
论文作者
论文摘要
建议采矿任务通常在语义上是复杂的，并且缺乏可应用于现实数据的复杂方法。在大量域上存在建议，并且缺少大型标记和平衡的数据集，这使得这项任务特别具有挑战性。为了克服这些挑战，我们提出了一条两层管道，该管道利用了基于话语标记的过度采样和细粒度的建议挖掘技术来从在线论坛中检索建议。通过对现实世界中的开放域建议数据集进行广泛的比较，我们演示了过采样技术如何与基于变压器的细粒分析结合使用，可以击败最新技术。此外，我们进行了广泛的定性和定性分析，以使我们提议的管道具有构造有效性。最后，我们讨论了管道在整个网络上部署的实用，计算和可重复性方面。
Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attempt to overcome these challenges, we propose a two-tier pipeline that leverages Discourse Marker based oversampling and fine-grained suggestion mining techniques to retrieve suggestions from online forums. Through extensive comparison on a real-world open-domain suggestion dataset, we demonstrate how the oversampling technique combined with transformer based fine-grained analysis can beat the state of the art. Additionally, we perform extensive qualitative and qualitative analysis to give construct validity to our proposed pipeline. Finally, we discuss the practical, computational and reproducibility aspects of the deployment of our pipeline across the web.
