Institut für Wirtschaftsinformatik
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/66
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Ergebnisse nach Hochschule und Institut
Publikation Multilingual Sentiment Analysis for a Swiss Gig(27.08.2018) Pustulka, Elzbieta; Hanne, Thomas; Blumer, Eliane; Frieder, Manuel; Wong, Ka ChunWe are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.04B - Beitrag KonferenzschriftPublikation Ensuring Action: Identifying Unclear Actor Specifications in Textual Business Process Descriptions(Springer, 2016) Sanne, Ulf; Ferrari, Alessio; Gnesi, Stefania; Witschel, Hans Friedrich; Ana, Fred; Aveiro, DavidIn many organisations, business process (BP) descriptions are available in the form of written procedures, or operational manuals. These documents are expressed in informal natural language, which is inherently open to different interpretations. Hence, the content of these documents might be incorrectly interpreted by those who have to put the process into practice. It is therefore important to identify language defects in written BP descriptions, to ensure that BPs are properly carried out. Among the potential defects, one of the most relevant for BPs is the absence of clear actors in action-related sentences. Indeed, an unclear actor might lead to a missing responsibility, and, in turn, to activities that are never performed. This paper aims at identifying unclear actors in BP descriptions expressed in natural language. To this end, we define an algorithm named ABIDE, which leverages rule-based natural language processing (NLP) techniques. We evaluate the algorithm on a manually annotated data-set of 20 real-world BP descriptions (1,029 sentences). ABIDE achieves a recall of 87%, and a precision of 56%. We consider these results promising. Improvements of the algorithm are also discussed in the paper.04B - Beitrag Konferenzschrift