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.

本文提出了稀疏有条件隐马尔可夫模型 (Sparse-CHMM)，来解决弱监督下命名实体识别中的标签函数可靠性评估问题。Sparse-CHMM通过估计每个标签函数的可靠性得分并以预定义的扩展函数将其扩展为发射矩阵，并在加权XOR分数的基础上增强此发射。 Sparse-CHMM通过无监督学习进行优化，并在五个综合数据集上取得了3.01个平均F1值的改善。

基于稀疏条件隐马尔可夫模型的弱监督命名实体识别