ідея - Digital Humanities - # Information Extraction Corpus

CHisIEC: Ancient Chinese History Information Extraction Corpus

Q: How can the availability of labeled data impact the development of deep learning models?

The availability of labeled data plays a crucial role in the development of deep learning models, especially for tasks like Named Entity Recognition (NER) and Relation Extraction (RE). With an adequate amount of high-quality labeled data, deep learning models can learn patterns and relationships effectively, leading to improved performance. Labeled data serves as a foundation for training these models, enabling them to understand the nuances and complexities present in the text data they are processing. Additionally, having access to diverse and well-annotated datasets allows researchers to train more robust models that can generalize better across different domains and scenarios.

Q: What are the implications of linguistic heterogeneity in ancient Chinese historical texts?

Linguistic heterogeneity in ancient Chinese historical texts poses several challenges but also offers unique insights into language evolution and cultural contexts. The variations in vocabulary, grammar, syntax, and writing styles across different periods make it challenging for natural language processing tasks such as NER and RE. Researchers need to account for these linguistic differences when developing algorithms or training models on such texts. Understanding linguistic heterogeneity is essential for accurate entity recognition and relation extraction since certain terms may have multiple meanings or contextual dependencies based on the time period or dynasty being referenced.

Q: How can large language models be effectively utilized for information extraction tasks beyond ancient Chinese history?

Large Language Models (LLMs) offer significant potential for information extraction tasks beyond ancient Chinese history due to their ability to process vast amounts of text data efficiently. These models can be fine-tuned on domain-specific datasets to adapt their knowledge base accordingly. By leveraging pre-trained LLMs like GPT-3 or ChatGLM2 with techniques such as In-Context Learning or Prompt Tuning, researchers can tailor these models for specific information extraction tasks in various domains. Furthermore, incorporating instruction-based learning strategies enables LLMs to follow task-specific guidelines provided as natural language instructions during fine-tuning processes. This approach enhances model performance by guiding them towards extracting structured information accurately from unstructured text sources outside ancient Chinese history contexts. Overall, by harnessing the power of large language models through effective fine-tuning methods tailored to specific information extraction requirements, researchers can achieve superior results across diverse domains beyond just ancient Chinese history datasets.

Основні поняття

CHisIEC provides a meticulously curated dataset for NER and RE tasks in ancient Chinese history, facilitating research in the field.

Анотація

Abstract: CHisIEC offers a dataset for NER and RE tasks in ancient Chinese history.
Introduction: Importance of digital humanities in preserving historical and cultural heritage.
Related Work: Studies on structuring historical documents and available datasets.
Information Extraction: Challenges in extracting information from ancient Chinese historical documents.
Corpus Annotation: Overview of CHisIEC corpus annotation process.
Experiments: Experimental results for NER and RE tasks with different models.
Conclusion: Summary of the proposed CHisIEC dataset.

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arxiv.org

Статистика

CHisIEC comprises 14,194 entities and 8,609 relations.
The dataset includes more than 130K tokens.

Цитати

"Natural Language Processing plays a pivotal role in Digital Humanities." - Xuemei Tang et al.

Ключові висновки, отримані з

CHisIEC

by Xuemei Tang,... о arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15088.pdf

Глибші Запити

How can the availability of labeled data impact the development of deep learning models?

The availability of labeled data plays a crucial role in the development of deep learning models, especially for tasks like Named Entity Recognition (NER) and Relation Extraction (RE). With an adequate amount of high-quality labeled data, deep learning models can learn patterns and relationships effectively, leading to improved performance. Labeled data serves as a foundation for training these models, enabling them to understand the nuances and complexities present in the text data they are processing. Additionally, having access to diverse and well-annotated datasets allows researchers to train more robust models that can generalize better across different domains and scenarios.

What are the implications of linguistic heterogeneity in ancient Chinese historical texts?

Linguistic heterogeneity in ancient Chinese historical texts poses several challenges but also offers unique insights into language evolution and cultural contexts. The variations in vocabulary, grammar, syntax, and writing styles across different periods make it challenging for natural language processing tasks such as NER and RE. Researchers need to account for these linguistic differences when developing algorithms or training models on such texts. Understanding linguistic heterogeneity is essential for accurate entity recognition and relation extraction since certain terms may have multiple meanings or contextual dependencies based on the time period or dynasty being referenced.

How can large language models be effectively utilized for information extraction tasks beyond ancient Chinese history?

Large Language Models (LLMs) offer significant potential for information extraction tasks beyond ancient Chinese history due to their ability to process vast amounts of text data efficiently. These models can be fine-tuned on domain-specific datasets to adapt their knowledge base accordingly. By leveraging pre-trained LLMs like GPT-3 or ChatGLM2 with techniques such as In-Context Learning or Prompt Tuning, researchers can tailor these models for specific information extraction tasks in various domains.
Furthermore, incorporating instruction-based learning strategies enables LLMs to follow task-specific guidelines provided as natural language instructions during fine-tuning processes. This approach enhances model performance by guiding them towards extracting structured information accurately from unstructured text sources outside ancient Chinese history contexts.
Overall, by harnessing the power of large language models through effective fine-tuning methods tailored to specific information extraction requirements, researchers can achieve superior results across diverse domains beyond just ancient Chinese history datasets.