Nested Named Entity Recognition with Partially-Observed TreeCRFs

15 Dec 2020  ·  Yao Fu, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang ·

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is difficult to detect entities with nested structures. In this work, we view nested NER as constituency parsing with partially-observed trees and model it with partially-observed TreeCRFs. Specifically, we view all labeled entity spans as observed nodes in a constituency tree, and other spans as latent nodes. With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes. To compute the probability of partial trees with partial marginalization, we propose a variant of the Inside algorithm, the \textsc{Masked Inside} algorithm, that supports different inference operations for different nodes (evaluation for the observed, marginalization for the latent, and rejection for nodes incompatible with the observed) with efficient parallelized implementation, thus significantly speeding up training and inference. Experiments show that our approach achieves the state-of-the-art (SOTA) F1 scores on the ACE2004, ACE2005 dataset, and shows comparable performance to SOTA models on the GENIA dataset. Our approach is implemented at: \url{}.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Nested Named Entity Recognition ACE 2004 PO-TreeCRFs F1 86.6 # 15
Nested Named Entity Recognition ACE 2005 PO-TreeCRFs F1 85.4 # 12
Nested Named Entity Recognition GENIA PO-TreeCRFs F1 78.2 # 16


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