Interpretability and Analysis of Models for Natural Language Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 16 March 2025 | Viewed by 269
Special Issue Editors
Interests: natural language processing; domain knowledge graph construction; fact analysis
Interests: big data analysis and mining; natural language processing; cloud network integration; network security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the rapid development of pre-trained model technology has profoundly transformed the fields of information retrieval (IR) and natural language processing (NLP). These technological advancements have driven significant improvements in various areas such as conversational systems, retrieval, ranking, knowledge acquisition, and representation learning for the extraction of information. Pre-trained models utilizing self-supervised language modeling are increasingly crucial, as recent studies show that these models can capture extensive linguistic and factual knowledge. This capability enhances downstream tasks, making the need to learn such knowledge from scratch obvious.
This Special Issue seeks to compile cutting-edge research contributions focused on the interpretability and analysis of models within the NLP domain. Our goal is to explore advanced technologies and methodologies that improve the understanding, transparency, and effectiveness of NLP models. We invite submissions that delve into theoretical, model-based, and application-driven perspectives, addressing the following topics, as well as others:
- Theoretical frameworks for model interpretability;
- Techniques for explaining the behavior and decisions of NLP models;
- Analytical methods for probing and understanding pre-trained models;
- Cross-disciplinary approaches combining IR and NLP for improved interpretability;
- Advances in conversational AI with a focus on transparency;
- Improved retrieval and ranking techniques through interpretable models;
- Innovations in knowledge acquisition and representation learning;
- Evaluation metrics and benchmarks for model interpretability.
Dr. Qing Li
Prof. Dr. Jiang Zhong
Dr. Jie Ma
Guest Editors
Manuscript Submission Information
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Keywords
- natural language processing
- information retrieval
- information extraction
- model interpretability
- model transparency
- evaluation metrics
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