利用词性嵌入实现建议挖掘中的远程监督
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings
摘要：
从给定文本中挖掘表达句子的建议是一个较少研究的句子分类任务，因此缺乏手工标记的基准数据集。在这项工作中，我们提出并评估了两种方法的建议挖掘远程监督。远程监督是通过使用wikiHow和Wikipedia的文本构建的大型银标准数据集获得的。这两种方法都使用基于LSTM的神经网络架构来学习建议挖掘的分类模型，但在使用银标准数据集的方法上有所不同。第一种方法使用该数据集直接训练分类器，而第二种方法仅从该数据集学习词嵌入。在第二种方法中，我们还学习了POS嵌入，有趣的是，它提供了最好的分类准确性。
英文摘要：
Mining suggestion expressing sentences from a given text is a less investigated sentence classification task, and therefore lacks hand labeled benchmark datasets. In this work, we propose and evaluate two approaches for distant supervision in suggestion mining. The distant supervision is obtained through a large silver standard dataset, constructed using the text from wikiHow and Wikipedia. Both the approaches use a LSTM based neural network architecture to learn a classification model for suggestion mining, but vary in their method to use the silver standard dataset. The first approach directly trains the classifier using this dataset, while the second approach only learns word embeddings from this dataset. In the second approach, we also learn POS embeddings, which interestingly gives the best classification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
