Document-Level Named Entity Recognition with Q-Network

Lu, Tingming; Gui, Yaocheng; Gao, Zhiqiang

doi:10.1007/978-3-030-29894-4_13

Cited by 1 publication

(2 citation statements)

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“…This paper extends our previous work RL-DNER [19] to interactive annotation setting, and proposes RL-DINEA. RL-DNER and RL-DINEA are approaches for different tasks-NER and interactive NE annotation, respectively.…”

Section: B Document-level Label Propagation For Named Entity Recognimentioning

confidence: 58%

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Learning Document-Level Label Propagation and Instance Selection by Deep Q-Network for Interactive Named Entity Annotation

Gui

Gao

2021

IEEE Access

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High quality annotated named entity corpora are essential language resources, but fully manual annotation is time-consuming. Interactive annotation offers an efficient alternative where humans and machines collaborate. Instances of named entity mentions tend to share the same label, when they cooccur in the same document and have similar surface forms. After selecting an instance in one sentence for manual annotation, the label of the instance can be propagated to instances in other sentences. This kind of document-level label propagation can be used to reduce human effort and improve annotation quality in interactive annotation. However, most existing literature assumes instances within different sentences are independent, and ignores document-level label propagation. This paper proposes a reinforcement learningbased approach, which learns to propagate labels among the instances within a document for interactive named entity annotation. In addition, our approach also learns instance selection for manual annotation. We optimize the objective which is a trade-off between human effort and annotation quality by training a deep Q-network. Our approach reduces human effort by more than 42% compared to baseline approaches, for achieving the same annotation quality (0.95 measured by F1 averaged on three datasets).

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Section: B Document-level Label Propagation For Named Entity Recognimentioning

confidence: 58%

“…Our previous work [19] proposes a reinforcement learningbased approach to document-level NER, short for RL-DNER. Candidate NEs with draft labels and estimated confidences are produced by ensemble of multiple existing NE taggers.…”

Section: B Document-level Label Propagation For Named Entity Recognimentioning

confidence: 99%