Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 3547–3557 August 1–6, 2021. ©2021 Association for Computational Linguistics 3547 Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path Yiran Wang1∗, Hiroyuki Shindo2, Yuji Matsumoto3, Taro Watanabe2 1National Institute of Information and Communications Technology (NICT), Kyoto, Japan 2Nara Institute of Science and Technology (NAIST), Nara, Japan 3RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan yiran.wang@nict.go.jp, shindo@is.naist.jp, yuji.matsumoto@riken.jp, taro@is.naist.jp Abstract This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second- best path recognition method by explicitly ex- cluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recogni- tion at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method. 1 Introduction Named entity recognition (NER), as a key tech- nique in natural language processing, aims at de- tecting entities and assigning semantic category labels to them. Early research (Huang et al., 2015; Ma and Hovy, 2016; Lample et al., 2016) proposed to employ deep learning methods and obtained significant performance improvements. However, most of them assume that the entities are not nested within other entities, so-called flat NER. Inherently, these methods do not work satisfactorily when nested entities exist. Figure 1 displays an example of the nested NER task. Recently, a large number of papers proposed novel methods (Fisher and Vlachos, 2019; Wang et al., 2020) for the nested NER task. Among them, layered methods solve this task through multi-level sequential labeling, in which entities are divided into several levels, where the term level indicates the depth of entity nesting, and sequential labeling is performed repeatedly. As a special case of lay- ered method, Shibuya and Hovy (2020) force the ∗This work was done when the first author was at NAIST. Former Hogwarts headmaster Dumbledore Albus ROLE ROLE ORG ROLE PER PER Figure 1: An example of nested NER. next level entities to locate on the second-best path of the current level search space. Hence, their algo- rithm can repeatedly detect inner entities through applying a conventional conditional random field (CRF) (Lafferty et al., 2001) and then exclude the obtained best paths from the search space. To accel- erate computation, they also designed an algorithm to efficiently compute the partition function with the best path excluded. Moreover, because they search the outermost entities first, performing the second-best path search only on the spans of ex- tracted entities is sufficient, since inner entities can only exist within outer entities. However, we claim that the target path at the next level is neither necessary nor likely to be the second-best …

Content truncated to 500 words