We investigate an approach to improving statistical text classification by combining machine learners with an ontology-based identification of domain-specific topic categories. We apply this approach to ad hoc disclosures by public companies. This form of obligatory publicity concerns all information that might affect the stock price; relevant topic categories are governed by stringent regulations. Our goal is to classify disclosures according to their effect on stock prices (negative, neutral, positive). In the study reported here, we combine natural language parsing with a formal background ontology to recognize disclosures concerning particular topics from a prescribed list. The semantic analysis identifies some of these topics with reasonable accuracy. We then demonstrate that machine learners benefit from the additional ontology-based information when predicting the cumulative abnormal return attributed to the disclosure at hand.

中文翻译：

---

在公司披露分类中结合机器学习和语义特征

我们研究了一种通过将机器学习器与基于本体的特定领域主题类别识别相结合来改进统计文本分类的方法。我们将这种方法应用于上市公司的临时披露。这种形式的强制性宣传涉及所有可能影响股价的信息；相关主题类别受严格的规定管辖。我们的目标是根据披露对股价的影响（负面、中性、正面）对披露进行分类。在这里报告的研究中，我们将自然语言解析与正式的背景本体相结合，以从规定的列表中识别有关特定主题的披露。语义分析以合理的准确性识别其中一些主题。