Does low birth rate affect China’s total factor productivity?

This study uses the DEA-Malmquist method to measure total factor productivity by employing the provincial panel data from 1998 to 2017 in China and constructing a panel data model to test the relationship between birth rate and human capital and the influence of labour in different age groups on total factor productivity. It was found that the increase in the birth rate has a significantly negative effect on human capital accumulation, while the effect of the birth rate on human capital shows an inverted ‘U’ shape. That is, when birth rate decreases, human capital increases, and when birth rate increases, human capital decreases. Thus, too low or too high birth rates will reduce human capital. Ultimately, human capital accumulation will significantly promote the growth and decomposition of total factor productivity. The effect of the labour age structure on total factor productivity also shows an inverted ‘U’ shape. Labour between 40 and 49 years old contributes the most to the promotion of total factor productivity. Eventually, due to the low birth rates, the proportion of 50- 59 years old will keep at high level. Therefore, total factor productivity will decline significantl

The influence of internal migration on regional innovation in China

Based on panel data from 2002 to 2017 in China, this paper analyses the influence of internal migration on regional innovation. The results show that internal migration not only has a significant promoting effect on improving regional innovation, but also presents a significant spatial agglomeration phenomenon. That is, internal migration has a significant positive impact on regional innovation according to the regression without spatial effects. And although internal migration will promote input of regional innovation, it will also have a negative impact on output of regional innovation. Meanwhile, internal migration will have a significant negative impact on innovation input in adjacent areas, and a significant positive impact on innovation output. Through decomposition, from the input of innovation, the migrant population will have a significant impact on local innovation, but it will also inhibit the innovation input of adjacent regions through indirect effects. Although the migrant population will have a significant negative impact on output of innovation, it will also promote innovation output significantly in adjacent regions through indirect effects, and have a positive impact on the improvement of overall innovation

LDA-Based Topic Strength Analysis

Topic strength is an important hotspot in topic research. The evolution of topic strength not only indicates emerging new topics, but also helps us to determine whether a topic will produce some fluctuation of topic strength over time. Thus, topic strength analysis can provide significant findings in public opinion monitoring and user personalization. In this paper, we present an LDA-based topic strength analysis approach. We take topic quality into our topic strength consideration by combining local LDA and global LDA. For empirical studies, we use three data sets in real applications: film critic data of "A Chinese Odyssey" in Douban Movies, corruption news data in Sina News, and public paper data. Compared to existing approaches, experimental results show that our proposed approach can obtain better results of topic strength analysis in detecting the time of event topic occurrences and distinguishing different types of topics, and it can be used to monitor the occurrences of public opinions and the changes of public concerns

Lexical Simplification with Pretrained Encoders

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks