Pattern-enhanced Named Entity Recognition with Distant Supervision
Pattern-enhanced Named Entity Recognition with Distant Supervision
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DOI：
10.1109/bigdata50022.2020.9378052
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发表时间：
2020-12
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影响因子：
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通讯作者：
Xuan Wang;Yingjun Guan;Yu Zhang;Qi Li;Jiawei Han
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文献类型：
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作者：
Xuan Wang;Yingjun Guan;Yu Zhang;Qi Li;Jiawei Han
有监督的深度学习方法在命名实体识别（NER）任务上取得了最先进的性能。然而，这样的方法在训练数据注释时存在高成本和低效率的问题，导致高度专业化的NER模型不能容易地适应新的领域。近年来，由于特定领域知识库的快速发展，远程监督已被应用于取代人工标注。然而，生成的噪声标签对学习具有远程监督的有效神经模型提出了重大挑战。我们提出了PatNER，一个远距离监督NER模型，有效地处理来自特定领域的字典的嘈杂的远距离监督。PatNER不需要人工标注的训练数据，只依赖于未标记的数据和不完整的特定于领域的字典进行远程监督。它将远程标记的不确定性纳入神经模型训练，以增强远程监督。我们超越了传统的序列标记框架，提出了一个更有效的模糊神经模型，使用领带或打破标签计划的NER任务。在两个领域的三个基准数据集上进行的大量实验证明了PatNER的强大功能。另外两个真实世界的数据集上的案例研究表明，PatNER提高了远NER在实体边界检测和实体类型识别的性能。结果表明，在支持高质量的命名实体识别领域特定的字典在各种各样的实体类型的承诺。
Supervised deep learning methods have achieved state-of-the-art performance on the task of named entity recognition (NER). However, such methods suffer from high cost and low efficiency in training data annotation, leading to highly specialized NER models that cannot be easily adapted to new domains. Recently, distant supervision has been applied to replace human annotation, thanks to the fast development of domain-specific knowledge bases. However, the generated noisy labels pose significant challenges in learning effective neural models with distant supervision. We propose PatNER, a distantly supervised NER model that effectively deals with noisy distant supervision from domain-specific dictionaries. PatNER does not require human-annotated training data but only relies on unlabeled data and incomplete domain-specific dictionaries for distant supervision. It incorporates the distant labeling uncertainty into the neural model training to enhance distant supervision. We go beyond the traditional sequence labeling framework and propose a more effective fuzzy neural model using the tie-or-break tagging scheme for the NER task. Extensive experiments on three benchmark datasets in two domains demonstrate the power of PatNER. Case studies on two additional real-world datasets demonstrate that PatNER improves the distant NER performance in both entity boundary detection and entity type recognition. The results show a great promise in supporting high quality named entity recognition with domain-specific dictionaries on a wide variety of entity types.