论文标题
矿工：从信息理论的角度提高名为“实体识别”的副总数
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
论文作者
论文摘要
NER模型已在标准NER基准测试中实现了有希望的性能。但是，最近的研究表明，以前的方法可能会过度依赖于实体提及信息，从而导致不良的汽车外（OOV）实体识别性能。在这项工作中，我们提出了一种新颖的NER学习框架Miner，以从信息理论的角度来解决这个问题。提出的方法包含两个基于信息的培训目标：i）概括信息最大化，从而通过对上下文和实体表面形式的深刻理解来增强表示形式； ii）多余的信息最小化，这阻止表示死记硬背的实体名称或利用数据中有偏见的提示。各种设置和数据集的实验表明，它在预测OOV实体方面取得了更好的性能。
NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.
