Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

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.

A review of the recent progress in battery informatics

“…text mining tools have been developed to harvest information from materials literature following the general overflow of acquiring text content, recognizing entities of interest, collecting and storing the entity information and performing post analysis and modeling (Fig. 2a) [81][82][83] . The usage of these tools generates libraries of information to explore, which forms the foundation for the designing and performing next phase research.…”

Opportunities and challenges of text mining in materials research

“…This significantly modifies the meaning of the tokens and usually results in lowered accuracy of the named entity recognition (see below). Currently, this problem is solved case-by-case by creating task-specific wrappers for existing tokenizers and named entity recognition models ( Huang and Ling, 2019 ; Alperin et al., 2016 ; He et al., 2020 ). Building a robust approach for chemistry-specific sentence tokenization and data extraction requires a thorough development of standard nomenclature for complex chemical terms and materials names.…”

“…Besides being intuitively simple, the main advantage of word embedding models is their ability to capture similarity and relations between words based on mutual associations. Word embeddings are applied ubiquitously in materials science TM and NLP to engineer words features that are used as an input in various named entity recognition tasks ( Kononova et al., 2019 ; Kim et al., 2020a ; Huang and Ling, 2019 ; Weston et al., 2019 ). Moreover, they also seem to be a promising tool to discover properties of materials through words association ( Tshitoyan et al., 2019 ).…”

Data-driven materials research enabled by natural language processing and information extraction

“…This involves recognizing the multi-word phrases in the chemical literature through unsupervised methods and then representing the phrases in the vocabulary. 73 Typically, word embedding is performed after tokenization with phrase representation obtained based on a post-vector addition. In this method, a new step is incorporated to identify multi-word phrases and add the detected terms to the vocabulary.…”