Chemical Reaction Extraction from Long Patent Documents
This work addresses the need for efficient patent search and knowledge base creation in chemistry, though it appears incremental as it builds on existing sequence tagging methods.
The paper tackled the problem of extracting reaction snippets from long chemical patent documents to build a reactions resource database, formulating it as a paragraph-level sequence tagging task and exploring various model modifications to study generalization across domains.
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.