Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10816423/ |
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| author | Sota Nemoto Shunsuke Kitada Hitoshi Iyatomi |
| author_facet | Sota Nemoto Shunsuke Kitada Hitoshi Iyatomi |
| author_sort | Sota Nemoto |
| collection | DOAJ |
| description | Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a single majority class (i.e., <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>-class). This imbalance leads to misclassifications of the entity classes as the <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>-class. To tackle this issue, we propose a simple and effective learning method named majority or minority (MoM) learning. MoM learning incorporates the loss computed only for samples whose ground truth is the majority class into the loss of the conventional ML model. Evaluation experiments on four NER datasets (Japanese and English) showed that MoM learning improves the prediction performance of the minority classes without sacrificing the performance of the majority class and is more effective than widely known and state-of-the-art methods. We also evaluated MoM learning using frameworks such as sequential labeling and machine reading comprehension, which are commonly used in NER. Furthermore, MoM learning has achieved consistent performance improvements regardless of language, model, framework, or data size. |
| format | Article |
| id | doaj-art-b38b92b043744a7e95b8b361317d4169 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b38b92b043744a7e95b8b361317d41692025-01-21T00:01:46ZengIEEEIEEE Access2169-35362025-01-01139902990910.1109/ACCESS.2024.352297210816423Majority or Minority: Data Imbalance Learning Method for Named Entity RecognitionSota Nemoto0https://orcid.org/0009-0006-0305-7127Shunsuke Kitada1https://orcid.org/0000-0002-3330-8779Hitoshi Iyatomi2https://orcid.org/0000-0003-4108-4178Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, Tokyo, JapanDepartment of Applied Informatics, Graduate School of Science and Engineering, Hosei University, Tokyo, JapanDepartment of Applied Informatics, Graduate School of Science and Engineering, Hosei University, Tokyo, JapanData imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a single majority class (i.e., <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>-class). This imbalance leads to misclassifications of the entity classes as the <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>-class. To tackle this issue, we propose a simple and effective learning method named majority or minority (MoM) learning. MoM learning incorporates the loss computed only for samples whose ground truth is the majority class into the loss of the conventional ML model. Evaluation experiments on four NER datasets (Japanese and English) showed that MoM learning improves the prediction performance of the minority classes without sacrificing the performance of the majority class and is more effective than widely known and state-of-the-art methods. We also evaluated MoM learning using frameworks such as sequential labeling and machine reading comprehension, which are commonly used in NER. Furthermore, MoM learning has achieved consistent performance improvements regardless of language, model, framework, or data size.https://ieeexplore.ieee.org/document/10816423/Natural language processingnamed entity recognitiondata imbalancecost-sensitive learning |
| spellingShingle | Sota Nemoto Shunsuke Kitada Hitoshi Iyatomi Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition IEEE Access Natural language processing named entity recognition data imbalance cost-sensitive learning |
| title | Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition |
| title_full | Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition |
| title_fullStr | Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition |
| title_full_unstemmed | Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition |
| title_short | Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition |
| title_sort | majority or minority data imbalance learning method for named entity recognition |
| topic | Natural language processing named entity recognition data imbalance cost-sensitive learning |
| url | https://ieeexplore.ieee.org/document/10816423/ |
| work_keys_str_mv | AT sotanemoto majorityorminoritydataimbalancelearningmethodfornamedentityrecognition AT shunsukekitada majorityorminoritydataimbalancelearningmethodfornamedentityrecognition AT hitoshiiyatomi majorityorminoritydataimbalancelearningmethodfornamedentityrecognition |