Manel Zarrouk

SemEval-2017 Task 5: Fine-grained sentiment analysis on financial microblogs and news

Horizon 2020 ICT Program Project SSIX: Social Sentiment analysis financial IndeXes, has received funding from the European Union’s Horizon 2020 Research and Innovation Program ICT 2014 - Information and Communications Technologies under grant agreement No. 645425

About Inferences in a Crowdsourced Lexical-Semantic Network

Automatically inferring new relations from already existing ones is a way to improve the quality of a lexical network by relation densification and error detection. In this paper, we devise such an approach for the JeuxDeMots lexical network, which is a freely avalaible lexical network for French. We first present deduction (generic to specific) and induction (specific to generic) which are two inference schemes ontologically founded. We then propose abduction as a third form of inference scheme, which exploits examples similar to a target term.

Spreading Relation Annotations in a Lexical Semantic Network Applied to Radiology

Domain specific ontologies are invaluable but their development faces many challenges. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of including domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength weights and annotations frequency information like frequent, rare ... or relevance information like pertinent or irrelevant. In this paper, we are presenting a semantic resource for radiology built over an existing general semantic lexical network JeuxDeMots. This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge.

WWW'18 Open Challenge: Financial Opinion Mining and Question Answering

The growing maturity of Natural Language Processing (NLP) techniques and resources is dramatically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The finance domain, with its reliance on the interpretation of multiple unstructured and structured data sources and its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques for the automatic analysis of financial news and opinions online. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain.

Sensemaking of complex sociotechnical systems: the case of governance dashboards

This research project is concerned with developing a suitable visualization model to depict a complex socio-technical system such as a city. It focuses on governance dashboards as the main starting point as these aim to depict many aspects of a city and, it is argued, already reflect and shape a city in its totality. Governance dashboards however pose a number of challenges and may not be the most suitable visualisation for representing a city. It is proposed to create a visualisation model that would fully capture a city in its complexity whilst being cognisant of allowing users to engage with detail.