Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

Hong Kong Corpus of Chinese Sentence and Passage Reading.

Recent years have witnessed a mushrooming of reading corpora that have been built by means of eye tracking. This article showcases the Hong Kong Corpus of Chinese Sentence and Passage Reading (HKC for brevity), featured by a natural reading of logographic scripts and unspaced words. It releases 28 eye-movement measures of 98 native speakers reading simplified Chinese in two scenarios: 300 one-line single sentences and 7 multiline passages of 5,250 and 4,967 word tokens, respectively. To verify its validity and reusability, we carried out (generalised) linear mixed-effects modelling on the capacity of visual complexity, word frequency, and reading scenario to predict eye-movement measures. The outcomes manifest significant impacts of these typical (sub)lexical factors on eye movements, replicating previous findings and giving novel ones. The HKC provides a valuable resource for exploring eye movement control; the study contrasts the different scenarios of single-sentence and passage reading in hopes of shedding new light on both the universal nature of reading and the unique characteristics of Chinese reading.