Dirichlet Compound Multinomials Statistical Models

• 作者: Paola Cerchiello Paolo Giudici
• 来源期刊: 应用数学(英文)
• 学科: 医学
• 关键词: TEXTUAL Data Analysis MIXTURE Models ONTOLOGY SCHEMA Reputational Risk
• 年，卷（期）: 2012,（12）
• 页码范围: 2089-2097
• 页数: 9页
• 分类号: R73

摘要

This contribution deals with a generative approach for the analysis of textual data. Instead of creating heuristic rules forthe representation of documents and word counts, we employ a distribution able to model words along texts considering different topics. In this regard, following Minka proposal (2003), we implement a Dirichlet Compound Multinomial (DCM) distribution, then we propose an extension called sbDCM that takes explicitly into account the different latent topics that compound the document. We follow two alternative approaches: on one hand the topics can be unknown, thus to be estimated on the basis of the data, on the other hand topics are determined in advance on the basis of a predefined ontological schema. The two possible approaches are assessed on the basis of real data.