论文标题
稀疏有条件隐藏的马尔可夫模型，用于弱监督的命名实体识别
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
论文作者
论文摘要
弱监督指定的实体识别方法训练标签模型，以汇总多个嘈杂标记功能（LFS）的令牌注释，而无需看到任何手动注释的标签。为了好的工作，标签模型需要在上下文上识别和强调良好的LF，同时使表现不佳的人减少。但是，由于缺乏地面真理，评估LFS是具有挑战性的。为了解决这个问题，我们提出了稀疏的有条件隐藏的马尔可夫模型（稀疏-CHMM）。稀疏-CHMM并没有将整个发射矩阵视为其他基于HMM的方法，而是专注于估计其对角线元素，后者被认为是LFS的可靠性得分。然后将稀疏分数扩展到具有预定义膨胀功能的全型发射矩阵。我们还通过加权XOR分数来增强发射，该得分跟踪LF观察不正确实体的概率。通过三阶段的训练管道通过无监督的学习来优化稀疏-CHMM，从而降低了训练难度并防止模型落入本地Optima。与扳手基准中的基线相比，稀疏-CHMM在五个综合数据集上取得了3.01的平均F1得分提高。实验表明，稀疏-CHMM的每个组件都是有效的，估计的LF可靠性与真实LF F1分数密切相关。
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels. To work well, the label model needs to contextually identify and emphasize well-performed LFs while down-weighting the under-performers. However, evaluating the LFs is challenging due to the lack of ground truths. To address this issue, we propose the sparse conditional hidden Markov model (Sparse-CHMM). Instead of predicting the entire emission matrix as other HMM-based methods, Sparse-CHMM focuses on estimating its diagonal elements, which are considered as the reliability scores of the LFs. The sparse scores are then expanded to the full-fledged emission matrix with pre-defined expansion functions. We also augment the emission with weighted XOR scores, which track the probabilities of an LF observing incorrect entities. Sparse-CHMM is optimized through unsupervised learning with a three-stage training pipeline that reduces the training difficulty and prevents the model from falling into local optima. Compared with the baselines in the Wrench benchmark, Sparse-CHMM achieves a 3.01 average F1 score improvement on five comprehensive datasets. Experiments show that each component of Sparse-CHMM is effective, and the estimated LF reliabilities strongly correlate with true LF F1 scores.
