2021, Pustulka, Elzbieta, Hanne, Thomas, de Espona, Lucía

FLIE (Form Labelling for Information Extraction) allows us to extract information from Swiss insurance policies. Insurance policies are forms which are weakly aligned and do not lend themselves to automated data extraction without preprocessing. Our preprocessing annotates data with geometry and combined with manual training data generation gives the extraction accuracy of over 80% for a subset of attributes which have been seen 8 times or more. In this paper we extend FLIE with rules. The aim is to compare machine learning used in FLIE to the standard industry approach of using rules to extract data. We hand crafted rules (regular expressions in Python) for the KTG insurance (27 rules), UVG insurance (29 rules), and UVG-Z (23 rules), for each insurance type covering around 20 attributes. We also generated rules for building insurance policies which we were new to (16 rules encoded in SpaCy). In all cases we saw that using rules alone gives us a similar accuracy in data extraction to machine learning (around 80%). In the case of building insurance the accuracy is higher, above 96%, with precision and recall around 89-92%. To support annotation and experimental evaluation, we created an annotation GUI and a GUI which automates the ML experiment. Planned work includes a comparison of rule based and ML approaches and extension to further policy types.

2016-09-05, Butty, Xavier, Stuber, Thomas, Hanne, Thomas, Dornberger, Rolf

2016-09-05, Lammel, Benjamin, Gryzlak, Karin, Hanne, Thomas, Dornberger, Rolf

2014-12-08T00:00:00Z, Fong, Simon, Zhuang, Yan, Deb, Suash, Hanne, Thomas

2019, Pustulka, Elzbieta, Hanne, Thomas

We work with a Swiss Gig Platform Company to identify innovative solutions which could strengthen its position as a market leader in Switzerland and Europe. The company mediates between employers and employees in short term work contracts via a platform system. We first looked at the business processes and saw that some process parts were not being controlled by the company, which is now being remedied. Second, we analyzed the job reviews which the employers and employees write, and implemented a prototype which can detect negative statements automatically, even if the review is positive overall. We worked with a dataset of 963 job reviews from employers and employees, in German, French and English. The reviews have a star rating (1 to 4 stars), with some discrepancies between the star rating and the text. We scored the reviews manually as negative or other, as negative reviews are important for business improvement. We tested several machine learning methods and a hybrid method from Lexalytics.

2016-09-05, Schädler, Philippe, Berdugo Valenzuela, Juan David, Hanne, Thomas, Dornberger, Rolf

2016-07-24, Hunkeler, Iris, Schär, Fabian, Hanne, Thomas, Dornberger, Rolf

2017-03-25, Sidler, Michael Martin, von Rohr, Christian, Hanne, Thomas, Dornberger, Rolf

2016-09-05, Meier, Peter, Künzli, Michael, Hanne, Thomas, Dornberger, Rolf

2016-07-24, Stauffer, Michael, Hanne, Thomas, Dornberger, Rolf