Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing

Yifei Yang, Zuchao Li, Hai Zhao


Abstract
As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling.
Anthology ID:
2022.coling-1.218
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2472–2482
Language:
URL:
https://aclanthology.org/2022.coling-1.218
DOI:
Bibkey:
Cite (ACL):
Yifei Yang, Zuchao Li, and Hai Zhao. 2022. Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2472–2482, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (Yang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.218.pdf
Code
 yangyifei729/nerasparsing
Data
GENIA