Band gap information extraction from materials science literature – a pilot study

Purpose The purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells. Design/methodology/approach The authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers. Findings The findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system. Originality/value The authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes

Improving Syntactic Parsing of Clinical Text Using Domain Knowledge

Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information. Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction