An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition
An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition
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DOI：
10.3115/1220175.1220316
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发表时间：
2006-07
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影响因子：
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通讯作者：
Vijay Krishnan;Christopher D. Manning
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文献类型：
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作者：
Vijay Krishnan;Christopher D. Manning
本文表明，一个简单的两阶段的方法来处理命名实体识别（NER）中的非本地依赖关系可以优于现有的方法，处理非本地依赖关系，同时更计算效率。NER系统通常使用序列模型进行易处理的推理，但这使得它们无法捕获文本中存在的长距离结构。我们使用基于条件随机场（CRF）的NER系统，使用本地特征进行预测，然后训练另一个CRF，它使用本地信息和从第一个CRF的输出中提取的特征。使用从同一文档中捕获非本地依赖关系的功能，我们的方法在F1得分上产生了12.6%的相对误差减少，与利用非本地信息的最佳系统提供的9.3%的相对误差减少相比，超过了仅使用本地信息的最先进的NER系统。我们的方法还可以很容易地将非本地信息从其他文件中的测试语料库，这给了我们一个13.3%的错误减少NER系统单独使用本地信息。此外，我们的推理运行时间只是两个顺序CRF的推理时间，这比直接建模的依赖关系和近似推理少得多。
This paper shows that a simple two-stage approach to handle non-local dependencies in Named Entity Recognition (NER) can outperform existing approaches that handle non-local dependencies, while being much more computationally efficient. NER systems typically use sequence models for tractable inference, but this makes them unable to capture the long distance structure present in text. We use a Conditional Random Field (CRF) based NER system using local features to make predictions and then train another CRF which uses both local information and features extracted from the output of the first CRF. Using features capturing non-local dependencies from the same document, our approach yields a 12.6% relative error reduction on the F1 score, over state-of-the-art NER systems using local-information alone, when compared to the 9.3% relative error reduction offered by the best systems that exploit non-local information. Our approach also makes it easy to incorporate non-local information from other documents in the test corpus, and this gives us a 13.3% error reduction over NER systems using local-information alone. Additionally, our running time for inference is just the inference time of two sequential CRFs, which is much less than that directly model the dependencies and do approximate inference.