Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks

• URL: http://arxiv.org/abs/2110.05419v1
• Date: Mon, 11 Oct 2021 17:01:43 GMT
• Title: Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks
• Authors: Songlin Yang and Kewei Tu
• Abstract summary: Constituency parsing and nested named entity recognition (NER) are typical textitnested structured prediction tasks. We propose a novel global pointing mechanism for bottom-up parsing with pointer networks to do both tasks, which needs linear steps to parse. Our method obtains the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing.
• Score: 24.337440797369702
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Constituency parsing and nested named entity recognition (NER) are typical \textit{nested structured prediction} tasks since they both aim to predict a collection of nested and non-crossing spans. There are many previous studies adapting constituency parsing methods to tackle nested NER. In this work, we propose a novel global pointing mechanism for bottom-up parsing with pointer networks to do both tasks, which needs linear steps to parse. Our method obtain the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and comparable performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA. Our code is publicly available at \url{https://github.com/sustcsonglin/pointer-net-for-nested}

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