基于实例的句法搜索引导关系抽取器
Bootstrapping Relation Extractors using Syntactic Search by Examples
摘要：
自然语言处理中神经网络的出现带来了监督式关系抽取的实质性改进。然而，获得足够数量的训练数据仍然是一个关键挑战。在这项工作中，我们提出了一个引导训练数据集的过程，可以由非NLP专家快速执行。我们利用搜索引擎的语法图（如Shlain et al.（2020）），它揭示了一个友好的例子语法。我们使用这些来获得积极的例子，通过搜索的句子，在句法上类似于用户输入的例子。我们将这种技术应用于TACRED和DocRED的关系，并表明所得到的模型与手动注释数据和从远程监督获得的数据上训练的模型具有竞争力。该模型还优于使用NLG数据增强技术训练的模型。使用NLG方法扩展基于搜索的方法进一步改善了结果。
英文摘要：
The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs (Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We use these to obtain positive examples by searching for sentences that are syntactically similar to user input examples. We apply this technique to relations from TACRED and DocRED and show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision. The models also outperform models trained using NLG data augmentation techniques. Extending the search-based approach with the NLG method further improves the results.
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