Fine-grained Dutch named entity recognition

被引:0
|
作者
Bart Desmet
Véronique Hoste
机构
[1] Ghent University,LT3, Language and Translation Technology Team
来源
关键词
Named entity recognition; Annotation; Classifier ensembles; Subtype classification;
D O I
暂无
中图分类号
学科分类号
摘要
This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and motivate our own, which describes six main types (persons, organizations, locations, products, events and miscellaneous named entities) and finer-grained information on subtypes and metonymic usage. This was applied to a one-million-word subset of the Dutch SoNaR reference corpus. The classifier for main type named entities achieves a micro-averaged F-score of 84.91 %, and is publicly available, along with the corpus and annotations.
引用
收藏
页码:307 / 343
页数:36
相关论文
共 50 条
  • [21] CHEMNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision
    Wang, Xuan
    Hu, Vivian
    Song, Xiangchen
    Garg, Shweta
    Xiao, Jinfeng
    Han, Jiawei
    2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), 2021, : 5227 - 5240
  • [22] Fine-grained multimodal named entity recognition with heterogeneous image-text similarity graphs
    Wang, Yongpeng
    Jiang, Chunmao
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2024, : 2401 - 2415
  • [23] Enhancing Legal Named Entity Recognition Using RoBERTa-GCN with CRF: A Nuanced Approach for Fine-Grained Entity Recognition
    Jain, Arihant
    Sharma, Raksha
    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT III, 2024, 14610 : 261 - 267
  • [24] NumER: A Fine-Grained Numeral Entity Recognition Dataset
    Julavanich, Thanakrit
    Aizawa, Akiko
    NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS (NLDB 2021), 2021, 12801 : 64 - 75
  • [25] Enhancing fine-grained geographic named entity recognition by Multi-scale Siamese Reconstruction Network
    Huang, Guanhua
    Gao, Bofei
    Chen, Jiaze
    Zhang, Yuchen
    Yang, Zhouwang
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 148
  • [26] MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
    Fetahu, Besnik
    Chen, Zhiyu
    Kar, Sudipta
    Rokhlenko, Oleg
    Malmasi, Shervin
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS - EMNLP 2023, 2023, : 2027 - 2051
  • [27] Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-Grained Student Ensemble
    Qu, Xiaoye
    Zeng, Jun
    Liu, Daizong
    Wang, Zhefeng
    Huai, Baoxing
    Zhou, Pan
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 11, 2023, : 13501 - 13509
  • [28] Fine-Grained Chinese Named Entity Recognition Based on MacBERT-Attn-BiLSTM-CRF Model
    Wang, Jueyang
    Li, Shuzhen
    Agyemang-Duah, Edward
    Feng, Xingyu
    Xu, Chun
    Ji, Yuao
    Liu, Junqiang
    2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2022, : 125 - 131
  • [29] SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
    Fetahu, Besnik
    Kar, Sudipta
    Chen, Zhiyu
    Rokhlenko, Oleg
    Malmasi, Shervin
    17TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2023, 2023, : 2247 - 2265
  • [30] Fine-Grained Crowdsourcing for Fine-Grained Recognition
    Jia Deng
    Krause, Jonathan
    Li Fei-Fei
    2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2013, : 580 - 587