Workshop: Tracing back biodiversity to the 19th century via text mining
Part of 49th Annual Meeting of the Ecological Society of Germany, Austria and Switzerland in Münster / Germany
Tuesday September 10th, 2019, 09:30 – 12:30 h
Fürstenberghaus, Domplatz 20-22, 48143 Münster / Germany
Lecturers
Christine Driller, Senckenberg Gesellschaft für Naturforschung
Giuseppe Abrami, Text Technology Lab, Goethe University Frankfurt
Manuel Stoeckel, Text Technology Lab, Goethe University Frankfurt
Christine Driller, Senckenberg Gesellschaft für Naturforschung
Giuseppe Abrami, Text Technology Lab, Goethe University Frankfurt
Manuel Stoeckel, Text Technology Lab, Goethe University Frankfurt
Christine Driller, Senckenberg Gesellschaft für Naturforschung
Abstract
Climate change and biodiversity loss are among the major challenges of our time. Investigating the causes and causal relationships are therefore the focus of current research and policy making. The extraction of biodiversity information from historical data sources becomes more and more relevant in this context in order to capture the baseline of the pre-industrial time. The Specialised Information Service Biodiversity Research (BIOfid) taps into this growing demand by improving the accessibility of legacy literature relevant to biodiversity research and by developing reusable text mining tools for data extraction. As part of the pilot project BIOfid particularly focusses on Central European literature on the distribution and ecology of vascular plants, birds, moths and butterflies. In framework of this workshop we want to share our technological developments and discuss researchers´ requirements and expectations for this type of information service. This workshop addresses scientists working in the field of biodiversity research and will focus on the following topics:
- Introduction to the BIOfid web portal, i.e. accessing literature and extracting data from historical texts through a visual interface.
- Use of state-of-the-art and easy-to-use Natural Language Processing (NLP) tools, e. g. deep learning of text content.
- Developing customer workflows from source materials to processable texts and data output.
The participants deal with challenges associated with data quality, natural language processing as well as information- and semantic relation extraction. They will automatically analyse large text corpora to extract data linked to established ontologies and knowledge bases. Participants are also invited to integrate their own texts into the analysis procedure provided by the authors.