Towards A Semantic Tagger for Analysing Contents of Chinese Corporate Reports

In this paper, we report on an experiment in which we explore the feasibility of applying a semantic tagger for analysing the textual contents of Chinese corporate reports, focusing on the contents of corporate strategy. In recent years, Natural Language Processing (NLP) research has been giving increasing attention to automatic analysis of the textual contents of corporate reports using NLP approach on a large scale. We test the assumption that semantic annotation tools can be useful for such a purpose, and study the feasibility by testing a Chinese semantic tagger developed by UCREL, Lancaster University for extracting core Chinese terms and semantic concepts from Chinese corporate annual disclosures, focusing on three main USAS semantic categories, Money & Commerce, Architecture & Buildings, and Science & Technology, which we assume are closely relevant to corporate strategy description, and use these categories and tags to extract core strategy terms. Our study shows that, by carefully applying selected semantic categories, our semantic annotation tool is capable of extracting core Chinese terms which can facilitate further content analysis of Chinese corporate reports.