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Paper on Classifying Semantic Types of Legal Sentences - Portability of Machine Learning Models has been accepted at Jurix 2018

The team of Florian Matthes, consisting of Ingo Glaser, and Elena Scepankova, has published their recent results on Classifying Semantic Types of Legal Sentences: Portability of Machine Learning Models at the 31st international conference on legal knowledge and information systems (Jurix). The paper is going to be presented at the conference from 12-14 December 2018, Netherlands, Groningen.

More details can be found here.

 


Paper titled Towards Explainable Semantic Text Matching accepted at JURIX 2018

The growing amount of textual data in the legal domain leads to a demand for better text analysis tools adapted to legal domain specific use cases. Semantic Text Matching (STM) is the general problem of linking text fragments of one or more document types. The STM problem is present in many legal document analysis tasks, such as argumentation mining. A common solution approach to the STM problem is to use text similarity measures to identify matching text fragments. In this paper, we recapitulate the STM problem and a use case in German tenancy law, where we match tenancy contract clauses and legal comment chapters. We propose an approach similar to local interpretable model-agnostic explanations (LIME) to better understand the behavior of text similarity measures like TFIDF and word embeddings. We call this approach eXplainable Semantic Text Matching (XSTM).


Paper titled Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing accepted at NL4AI

Paper titled Paper titled Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing accepted at NL4AI.

Understanding and analysing rapidly changing and growing business ecosystems, like smart city and mobility ecosystems, becomes increasingly difficult. However, the understanding of these ecosystems is the key to being successful for all involved parties, like companies and public institutions. Modern Natural Language Processing technologies can help to automatically identify and extract relevant information from sources like online news and blog articles and hence support the analysis of complex ecosystems. In this paper, we present an approach to automatically extract directed relations between entities within business ecosystems from online news and blog articles by using dependency parsing.

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Paper titled A Machine Learning Based Approach to Application Landscape Documentation accepted at PoEM 2018

In the era of digitalization, IT landscapes keep growing along with complexity and dependencies. This amplifies the need to determine the current elements of an IT landscape for the management and planning of IT landscapes as well as for failure analysis. The field of enterprise architecture documentation seeked for more than a decade for solutions to minimize the manual effort to build enterprise architecture models or automation. We summarize the approaches presented in the last decade in a literature survey. Moreover, we present a novel, machine-learning based approach to detect and to identify applications in an IT landscape.

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Paper titled A Holistic Model-based Adaptive Case Management Approach for Healthcare accepted at AdaptiveCM 2018

In recent years, personalized connected care has become increasingly important due to the generally aging population and the rising cost pressures in the healthcare sector. Nevertheless, to the best of our knowledge, there are no off-the-shelf solutions available to provide open and adaptable information and communication technology (ICT) for connected care; the reasons for this may differ between use cases. Based on our case studies at hospitals in different European countries, we identified three main challenges of such for ICT solution: 1) the high diversity between hospital sites and treatments, 2) the embedding of information from existing information systems, and 3) the coordination and communication of the many different stakeholders. Our approach is to use a full stack modelbased solution that supports the integration, communication, and coordination of the pending work. Currently, our solution is being used for clinical trials. [link]