Learning Named Entity Tagger using Domain-Specific Dictionary
This work addresses the challenge of reducing manual labeling effort for NER systems, making it more accessible for domains with limited annotated data, though it is incremental in improving existing distant supervision methods.
The authors tackled the problem of noisy labels in distant supervision for named entity recognition by proposing two neural models, including AutoNER with a Tie or Break scheme, which achieved state-of-the-art performance on three benchmark datasets using only dictionaries without human annotations.
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.