In this work, we examine the auditor opinions that are provided in financial reports of public companies, when they express issues related to going concerns. Auditor opinions provide explicit insights regarding potential threats to the financial status of companies. We, therefore, provide methods for the automated classification of the auditor narratives to going concern issues, and we investigate which of those issues are related to bankruptcy events. We focus on annual reports of public US companies and publicly available bankruptcy labels to learn models that label these reports with probable going concern issues in an automated way. Our experimental results validate our approach and provide evidence that the analysis of these narratives can lead to the identification of specific issues related to bankruptcy concerns and thus alarm the interested parties.

This paper presents the results and findings of the Financial Narrative Summarisation Shared Task on summarising UK, Greek, and Spanish annual reports. The shared task was organised as part of the 5th Financial Narrative Processing Workshop (FNP 2023). The Financial Narrative summarisation Shared Task (FNS 2023) has been running since 2020 as part of the Financial Narrative Processing (FNP) workshop series [15–20]. The shared task included one main challenge, which is the use of either abstractive or extractive automatic summarisers to summarise long documents in terms of UK, Greek, and Spanish financial annual reports. This shared task is the fourth to target financial documents. The data for the shared task was created and collected from publicly available annual reports published by firms listed on the Stock Exchanges of the UK, Greece, and Spain. A total number of 6 systems from 3 different teams participated in the shared task.

This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.

The requirement for ubiquitous service access in wireless environments presents a great challenge in light of well-known problems like high error rate and frequent disconnections. In order to satisfy this requirement, we propose the integration of two modern service technologies: Web Services and Mobile Agents. This integration allows wireless users to access and invoke semantically enriched Web Services without the need for simultaneous, online presence of the service requestor. Moreover, in order to improve the capabilities of Service registries, we exploit the advantages offered by the Semantic Web framework. Specifically, we use enhanced registries enriched with semantic information that provide semantic matching to service queries and published service descriptions. Finally, we discuss the implementation of the proposed framework and present our performance assessment findings.

This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies concepts in documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. The method uncovers latent topics in terms of which document text is being generated. These topics form the concepts of the new ontology. This is done in a language neutral way, using probabilistic space reduction techniques over the original term space of the corpus. Given multiple sets of concepts (latent topics) being discovered, the proposed method constructs a subsumption hierarchy by performing conditional independence tests among pairs of latent topics, given a third one. The paper provides experimental results over the GENIA corpus from the domain of biomedicine.