Abstract
Abstract
In recent years, data-driven methods and artificial intelligence have been widely used in chemoinformatic and material informatics domains, for which the success is critically determined by the availability of training data with good quality and large quantity. A potential approach to break this bottleneck is by leveraging the chemical literature such as papers and patents as alternative data resources to high throughput experiments and simulation. Compared to other domains where natural language processing techniques have established successes, the chemical literature contains a large portion of phrases of multiple words that create additional challenges for accurate identification and representation. Here, we introduce a chemistry domain suitable approach to identify multiword chemical terms and train word representations at the phrase level. Through a series of special-designed experiments, we demonstrate that our multiword identifying and representing method effectively and accurately identifies multiword chemical terms from 119, 166 chemical patents and is more robust and precise to preserve the semantic meaning of chemical phrases compared to the conventional approach, which represents constituent single words first and combine them afterward. Because the accurate representation of chemical terms is the first and essential step to provide learning features for downstream natural language processing tasks, our results pave the road to utilize the large volume of chemical literature in future data-driven studies.