Abstract
The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. In many domains–such as medical, legal, and HR–the extraction model must also adapt to shifting terminology and benefit from explicit reasoning over structured knowledge. We propose DySECT, a Dynamic Self-Evolving Extraction and Curation Toolkit, which continually improves as it is used. The system incrementally populates a versatile, self-expanding knowledge base (KB) with triples extracted by the LLM. The KB further enriches itself through the integration of probabilistic knowledge and graph-based reasoning, gradually accumulating domain concepts and relationships. The enriched KB then feeds back into the LLM extractor via prompt tuning, sampling of relevant few-shot examples, or fine-tuning using KB-derived synthetic data. As a result, the system forms a symbiotic closed-loop cycle in which extraction continuously improves knowledge, and knowledge continuously improves extraction.- Anthology ID:
- 2026.acl-demo.69
- Volume:
[Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)](/volumes/2026.acl-demo/)- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
[Greg Durrett](/people/greg-durrett/unverified/),[Ping Jian](/people/ping-jian/)- Venue:
[ACL](/venues/acl/)- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 702–714
- Language:
- URL:
[https://aclanthology.org/2026.acl-demo.69/](https://aclanthology.org/2026.acl-demo.69/)- DOI:
- Cite (ACL):
- Moin Aminnaseri, Hannah Kim, and Estevam Hruschka. 2026. A Dynamic Self-Evolving Extraction System. InProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 702–714, San Diego, California, United States. Association for Computational Linguistics. - Cite (Informal):
[A Dynamic Self-Evolving Extraction System](https://aclanthology.org/2026.acl-demo.69/)(Aminnaseri et al., ACL 2026)- PDF:
[https://aclanthology.org/2026.acl-demo.69.pdf](https://aclanthology.org/2026.acl-demo.69.pdf)