Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Table 1: Token-Level F1 scores on the test set. The unmodified PU algorithm achieves an average F1 score of 68.95%. Integrating dependency parsing (Dep) and iterative relabeling (Iter) raises the F1 score to 72.02%, an improvement of 42.08% over a dictionary-only approach, and 3.63% over a BiLSTM baseline.
['Entity Type', 'Dictionary', 'BiLSTM', 'PU', 'PU+Dep', 'PU+Iter', 'PU+Dep+Iter']
From the table, we can observe: 1) The PU algorithm outperforms the simpler models for most classes, which demonstrates the effectiveness of the PU learning framework for NER in our domain. 2) Dependency parsing is a very effective feature for Component and Product, and it strongly improves the overall F1 score. 3) The iterative training strategy yields a significant improvement for most classes.