Our empirical results demonstrate the semantic information integration induce an decline of stock price synchronicity, stronger semantic information integration attracts higher media coverage and greater analyst market attention, and produces greater reduction on stock price synchronicity than...

Social scientists have become increasingly interested in how narratives -- the stories in fiction, politics, and life -- shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our pipeline identifies...

Network models are one of the methods used by linguists, psychologists and cognitive scientists to represent the semantic organization structure. In order to understand the semantic organization development of Chinese preschool children in their actual use of language, this paper constructs...

This paper studies economic determinants of semantic similarity in executive compensation disclosures and its implications for compensation peer selection. We employ a novel measure based on a natural language processing algorithm with machine learning---document embeddings---to capture nuanced...

This paper applies data science techniques, specifically natural language processing algorithms, to a corpus comprised of the Bank of England’s MPC minutes. Whilst acknowledging that there is a considerable amount of fuzziness surrounding the use of such techniques, there is some evidence to...

This paper considers a model of the neural network for semantically segmenting the images of monitored objects on aerial photographs. Unmanned aerial vehicles monitor objects by analyzing (processing) aerial photographs and video streams. The results of aerial photography are processed by the...