Abstract
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.
Highlights
Extensive efforts have been made on building reliable named entity recognition (NER) models without handcrafting features (Liu et al, 2018; Ma and Hovy, 2016; Lample et al, 2016)
We propose AutoNER, a novel neural model with the new Tie or Break scheme for the distantly supervised NER task
We design and explore two neural models, Fuzzy-LSTM-conditional random field (CRF) with the modified IOBES scheme and AutoNER with the Tie or Break scheme, to learn named entity taggers based on such labels with unknown and multiple types
Summary
Extensive efforts have been made on building reliable named entity recognition (NER) models without handcrafting features (Liu et al, 2018; Ma and Hovy, 2016; Lample et al, 2016). Most existing methods require large amounts of manually annotated sentences for training supervised models (e.g., neural sequence models) (Liu et al, 2018; Ma and Hovy, 2016; Lample et al, 2016; Finkel et al, 2005). 1 https://github.com/shangjingbo1226/ AutoNER of various domain-specific systems in a plug-inand-play manner
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