About this Research Topic

Background

The theme of "Structured Information Extraction in the context of Large Language Models (LLMs)" revolves around the intersection of natural language processing (NLP) and large language models. Large language models, such as GPT-3, have demonstrated remarkable capabilities in understanding and generating human-like text. However, extracting structured information from unstructured text remains a challenge. This article collection aims to explore advancements in structured information extraction techniques within the context of LLMs. Topics of interest include methods for named entity recognition, relation extraction, semantic parsing, and entity linking, among others.

The goal of this collection is to explore the advancements and potential of leveraging large language models for extracting structured information from unstructured text. The aim is to showcase innovative research and methodologies that harness the power of LLMs to overcome the challenges of information extraction, such as named entity recognition, relation extraction, and semantic parsing. By emphasizing structured representations and knowledge extraction, this Research Topic seeks to advance the field of natural language processing and facilitate the development of applications like knowledge graphs, information retrieval, and data-driven decision-making. The Research Topic invites researchers from diverse domains, including NLP, machine learning, and data mining, to contribute their novel approaches, empirical studies, and theoretical insights. Ultimately, the goal is to foster interdisciplinary collaboration and drive the frontier of structured information extraction techniques within the context of LLMs.

Submissions are invited on a wide range of topics related to Structured Information Extraction in the context of Large Language Models (LLMs), including but not limited to:
1. Architectures and models for large language models
2. Training methodologies and techniques
3. Pre-training and fine-tuning approaches
4. Multilingual and cross-lingual models
5. Domain adaptation and transfer learning
6. Applications of large language models in:
- Procedural text mining
- Knowledge graph construction
- Named entity extraction, relation extraction, entity and relation linking
- Ontology and schemata discovery
- Semantic web practices
- Question answering and knowledge base completion
- Code generation and software engineering
8. Evaluation metrics and benchmarks for large language models

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Semantic Parsing, Named Entity Recognition (NER), Relation Extraction, Entity Linking, Structured Information Extraction, Large Language Models (LLMs), Natural Language Processing (NLP), Information Retrieval, Knowledge Graphs

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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