Siegfried Handschuh

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

DINFRA: A One Stop Shop for Computing Multilingual Semantic Relatedness

This demonstration presents an infrastructure for computing multilingual semantic relatedness and correlation for twelve natural languages by using three distributional semantic models (DSMs). Our demonsrator - DInfra (Distributional Infrastructure) provides researchers and developers with a highly useful platform for processing large-scale corpora and conducting experiments with distributional semantics. We integrate several multilingual DSMs in our webservice so end user can obtain a result without worrying about the complexities involved in building DSMs. Our webservice allows the users to have easy access to a wide range of comparisons of DSMs with different parameters. In addition, users can configure and access DSM parameters using a easy to use API.

Analysis of cyberbullying tweets in trending world events

The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.

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.

The Robot Who Loved Me: Building Consciousness Models for Use in Human Robot Interaction Following a Collaborative Systems Approach

We build on the results of our MARIO project to support the benefit of informing research taking place in the area of development of human consciousness models for use by robots to facilitate human robot interaction with research results from the area of collaborative systems. The main outcome of such a research would be a software model of a robot assistant that might offer functionality and features of a ‘virtual coach’ with an in-built evolution capability.

Longinos/Longinas: Towards Smart, Unified Working and Living Environments for the 70 to 90+

The ageing of the human population is a threat to many countries in the world and this fact creates new challenges for age-friendly living, recreational and working environments. Therefore, solutions that can support senior citizens (Longinos for the men and Longinas for the women) will be necessary, in order to help them stay actively involved in their professional life for longer. This is possible by designing fit for purpose working environments and by enabling flexible management of job-, leisure- and health-related activities, considering their needs at the workplace, at home and on the move, with a particular focus on fighting social isolation. This project presents a robotic digital solution that makes provision for Longinos/Longinas persons, that are above 70 years old, a single view of integrated health, business and social data spread respectively in online health communities, online project management websites and social networks, as well as the provision of a set of services, that will allow them to manage huge amounts of data.

Classification of composite semantic relations by a distributional-relational model

Abstract Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases, it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification using one or more relations between entities/term mentions, extending the traditional semantic relation classification task. The proposed model is different from existing approaches which typically use machine learning models built over lexical and distributional word vector features in that is uses a combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem. The proposed approach outperformed existing baselines with regard to F1-score, Accuracy, Precision and Recall.