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Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition

Min Zhang1,2, Guohua Geng1, Jing Chen1

  • 1School of Information Science and Technology, Northwest University, Xi'an 710127, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
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This summary is machine-generated.

This study introduces SCRNER, a semi-supervised deep learning model for recognizing named entities in cultural relics data. It effectively uses limited labeled data and abundant unlabeled data for improved cultural heritage knowledge discovery.

Area of Science:

  • Digital Humanities
  • Artificial Intelligence
  • Cultural Heritage Informatics

Background:

  • Online museums generate vast cultural relics data, crucial for knowledge acquisition.
  • Deep learning models for Named Entity Recognition (NER) require substantial labeled data, which is scarce in the cultural relics domain.
  • Existing NER models struggle with the unique challenges of cultural relics data, including blurred object boundaries and specific textual characteristics.

Purpose of the Study:

  • To develop an effective Named Entity Recognition (NER) model for cultural relics using limited labeled data.
  • To address the data scarcity problem in cultural heritage informatics by leveraging semi-supervised learning.
  • To improve the extraction of cultural knowledge from online museum data.

Main Methods:

Keywords:
bidirectional long short-term memory networkconditional random fieldscultural relicsembeddings from language modelsnamed-entity recognitionsemi-supervised learning

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  • Proposed a semi-supervised deep learning model named SCRNER (Semi-supervised model for Cultural Relics' Named Entity Recognition).
  • Utilized a combination of bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF).
  • Implemented a repeat-labeled (relabeled) strategy for sample selection and employed ELMo (Embeddings from Language Model) for dynamic word representations.

Main Results:

  • The SCRNER model achieved effective performance in Named Entity Recognition (NER) for cultural relics.
  • Demonstrated the viability of semi-supervised learning with limited labeled data in this specialized domain.
  • Successfully handled the complexities of cultural object descriptions and Chinese text characteristics.

Conclusions:

  • SCRNER offers a robust solution for NER on cultural relics, overcoming data limitations.
  • The proposed model enhances the accessibility and analysis of cultural heritage data.
  • This approach contributes to advancing digital humanities research through improved information extraction.