English

Chemical Reaction Extraction from Long Patent Documents

Information Retrieval 2024-07-24 v2 Artificial Intelligence Machine Learning

Abstract

The task of searching through patent documents is crucial for chemical patent recommendation and retrieval. This can be enhanced by creating a patent knowledge base (ChemPatKB) to aid in prior art searches and to provide a platform for domain experts to explore new innovations in chemical compound synthesis and use-cases. An essential foundational component of this KB is the extraction of important reaction snippets from long patents documents which facilitates multiple downstream tasks such as reaction co-reference resolution and chemical entity role identification. In this work, we explore the problem of extracting reactions spans from chemical patents in order to create a reactions resource database. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs that contain a description of a reaction. We propose several approaches and modifications of the baseline models and study how different methods generalize across different domains of chemical patents.

Cite

@article{arxiv.2407.15124,
  title  = {Chemical Reaction Extraction from Long Patent Documents},
  author = {Aishwarya Jadhav and Ritam Dutt},
  journal= {arXiv preprint arXiv:2407.15124},
  year   = {2024}
}

Comments

Work completed in 2022 at Carnegie Mellon University

R2 v1 2026-06-28T17:48:42.110Z