School of Business and Management Department of Information Systems, Business Statistics and Operations Management 194 Deep Learning NLP in finance Supervisor: YANG Yi / ISOM Student: WANG Xiaopeng / DSCT Course: UROP1100, Summer Existing NLP systems rarely separate numbers and words in the text when embedding them. This obviously contrasts with the consensus in neuroscience that the human’s brains treat numbers differently from words. For instance, most NLP models are not specially designed for processing numbers, most of which either discard numbers directly or replace them with UNK tokens. However, given the ubiquity of numbers especially in the financial area, it would be beneficial for us to enable NLP models to understand numbers more effectively. Deep Learning NLP in finance Supervisor: YANG Yi / ISOM Student: ZHOU Zhuorui / COGBM Course: UROP1100, Spring Earnings conference calls are the most interactive ways between companies and investor. To objectively evaluate the performance of different business components for a company, it is a common practice to categorize different sectors within analyzed company’s business. While the overall objective of this research projects aims to automatically categorize business topics for given company and analyze the sentiment of company’s performance accordingly, this report mostly comprises of exploratory analysis and serves as preparation for the overall project. The report focuses on models, such as LDA, InfoMap, experimented for the “topic selection” part. Latest development in topic models is also introduced in this report.
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