SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

Financial advice is given by a registered financial adviser (RFA) in the form of a statement of advice (SoA) document. To limit liability, financial advisor groups periodically assess SoA documents for compliance with legal regulations, internal policies, and best practices. However, this is a manual process that is often subjective, time-consuming and tedious. In this paper, we propose, implement and evaluate SoA Risk Auditor (SRAuditor), a natural language processing (NLP) framework to automatically assess and audit SoA documents. SRAuditor consists of two major components. The first one is a SoA transformer (SoA-T), a tool that automatically transforms and maps SoA document (generally a PDF). The other one is a question-answering engine (QA-R) that recommends legally compliant answers based on rule-based approaches for given SoA audit questions to assess and audit SoA documents. We validate the accuracy of SRAuditor's ability by evaluating it against assessments conducted by domain experts (i.e., financial advisors, lawyers). Experimental results using real-world SoA documents provided by our industry partner, Fourth Line Pty Limited indicate that SRAuditor has a high potential to be used for automatically assessing and auditing SoA documents

AQUA: an ontology driven question answering system

This paper describes AQUA our question answering over the Web. AQUA was designed to work over heterogeneous sources. This means that AQUA is equipped to work as closed domain and in addition to open-domain question answering. As a first instance, AQUA tries to answer a question using a Knowledge base. If a query cannot be satisfied over a knowledge base/database. Then, AQUA tries to find an answer on web pages (i.e. it uses as corpus the internet as resource). Our system uses NLP (Natural Language Processing), First order logic and Information Extraction technologies. AQUA has been tested using an ontology which describes academic life. Keywords Ontologies, Information Extraction, Machine Learnin

Parsing Thai Social Data: A New Challenge for Thai NLP

Dependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the appropriate linguistic unit as an input. The result showed that, the transition based model called, improve Elkared dependency parser outperform the others at UAS of 81.42%.Comment: 7 Pages, 8 figures, to be published in The 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2019