Helmi Hammami

General learning approach for event extraction: Case of management change event

Starting from an ontology of a targeted financial domain corresponding to transaction, performance and management change news, relevant segments of text containing at least a domain keyword are extracted. The linguistic pattern of each segment is automatically generated to serve initially as a learning model. Each pattern is composed of named entities, keywords and articulation words. Some generic named entities like organizations, persons, locations, dates and grammatical annotations are generated by an automatic tool. During the learning step, each relevant segment is manually annotated with respect to the targeted entities (roles) structuring an event of the ontology. Information extraction is processed by associating a role with a specific entity. By alignment of generic entities to specific entities, some strings of a text are automatically annotated. An original learning approach is presented. Experiments with the management change event showed how recognition rates are improved by using different generalization tools.

Accounting narratives' characteristics and firm performance in the MD&As of listed Italian companies

The objective of this research is to investigate the textual construction of annual reports (MDA b) wording structure. Tests are made to measure the level of readability and wording construction of the MD&A of 40 listed companies. Results indicate that narratives are assessed as difficult to read independently of firm financial performance opening interpretations about how much attention is given to writing style in the reporting process. On the other hand, less performers seem using positive words in excess, giving too much positive colouration to their messages which may open way to several questions about intentional choice of texts and financial performance.

Financial events detection by conceptual news categorization

In the scope of Financial Watch project, several targeted events have been required by contacted users in banking and investment domains. Financial news are classified with respect of the list of desired events. In this paper, a conceptual approach for indexing short English news in the financial domain is presented. By using a supervised original learning approach, a categorization method is proposed. Experimentation of the method on a sample of news showed that in almost all cases, occurrences of the right events in each document have been recognized, with respect to a corpus on the financial domain.

Do users in Qatar perceive intellectual capital information to be value relevant for decision-making purposes?

This paper examines the perception of users on the value relevance of intellectual capital (IC) information for decision-making. The data were collected through questionnaires distributed to Qatari companies. The paper found that users perceive that IC information, internal capital information, and external capital information are relevant for decision-making. Thus, it has value relevance. Human capital was not perceived as relevant. The findings are useful for companies to better understand the needs of information users. They assist regulatory bodies in Qatar towards developing guidelines and standards for better corporate disclosure of IC, thereby helping in decision-making. This is the first research that examines the value relevance of IC information in Qatar.

Does political instability affect exchange rates in Arab Spring countries

ABSTRACT This article investigates the dynamic relationship between political instability and exchange rates in five Arab Spring countries over the period 1992Q1–2016Q4. We include macroeconomic fundamentals to identify the transmission channels through which political instability may affect exchange rates. Based on VAR and ARDL models, our results report that political instability is associated with a significant drop in the value of domestic currencies of these countries. Economic growth is found to be the key mechanism channel. We find also that the dependence between variables is more emphasized in the short run than in the long run.

Conceptual ascendant feature extraction of a financial corpus

In the scope of Financial Watch project, in order to create a feature of a corpus reflecting its semantic structure, a general algorithm of text micro structuring and browsing is proposed. The same conceptual algorithm has been extended recursively to derive the macro feature of a space of documents, in an ascendant way. Obtained browsing system based on the extracted feature proved to help for a fast convergence to the user request. The same tool is extendable for report generation from a corpus, based on a general feature. The main goal of this paper is to propose new experiences on document feature composition, in order to compare different varieties of algorithms and find out better paradigms with respect to some well defined criteria.

A multilingual financial watch alerting system

AbstractDepending on user profiles expressed by associated expected events, and conditions for raising alerts, from manual meticulous news annotation of an adequately selected corpus, an ontology domain is created. A cross-language information retrieval approach is used for automatic translation of financial documents corresponding to the particular domain corresponding to the user requirement. By this way, users may receive alerts and news expressed in their own language even if they are initially expressed in a different language. Manual annotation is used for knowledge extraction composed of general rules useful for automatic annotation of financial news arriving instantly to the system from reliable providers of information. As a first step in the loop, news are filtered, split into different sub-documents each one corresponding to a particular event and categorized. News are then mapped automatically to formatted data as instantiations of a sequence of predefined entities defined an event. By using a...