ChemTables: A Dataset for Semantic Classification of Tables in Chemical Patents.

Zenan Zhai, Christian Drukenbrodt, Camilo Thorne, Saber Akhondi, Dat Quoc Nguyen, Trevor Cohn, Karin Verspoor

Research output: Contribution to journalArticlepeer-review

Abstract

Chemical patents are a commonly used channel for disclosing novel compounds and reactions, and hence represent important resources for chemical and pharmaceutical research. Key chemical data in patents is often presented in tables. Both the number and the size of tables can be very large in patent documents. In addition, various types of information can be presented in tables in patents, including spectroscopic and physical data, or pharmacological use and effects of chemicals. Since images of Markush structures and merged cells are commonly used in these tables, their structure also shows substantial variation. This heterogeneity in content and structure of tables in chemical patents makes relevant information difficult to find.

We therefore propose a new text mining task of automatically categorising tables in chemical patents based on their contents. Categorisation of tables based on the nature of their content can help to identify tables containing key information, improving the accessibility of information in patents that is highly relevant for new inventions.

For developing and evaluating methods for the table classification task, we developed a new dataset, called ChemTables, which consists of 7,886 chemical patent tables with labels of their content type. We introduce this data set in detail. We further establish strong baselines for the table classification task in chemical patents by applying state-of-the-art neural network models developed for natural language processing, including TabNet, ResNet and Table-BERT on ChemTables. The best performing model, Table-BERT, achieves a performance of 88.66 micro F1 score on the table classification task.

Availability: A 10% sample of the ChemTables dataset has been made publicly available, subject to a data usage agreement.
Original languageAmerican English
JournalResearch Square preprint
DOIs
StatePublished - 2021

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