Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.Comment: Accepted for publication in the 25th International Conference on Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken Germany, June 24-26 202

Next-generation sequencing-based genome diagnostics across clinical genetics centers: Implementation choices and their effects

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome- and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care

A Lexico-Semantic Pattern Language for Learning Ontology Instances from Text

The Semantic Web aims to extend the World Wide Web with a layer of semantic information, so that it is understandable not only by humans, but also by computers. At its core, the Semantic Web consists of ontologies that describe the meaning of concepts in a certain domain or across domains. The domain ontologies are mostly created and maintained by domain experts using manual, time-intensive processes. In this paper, we propose a rule-based method for learning ontology instances from text that helps domain experts with the ontology population process. In this method we define a lexico-semantic pattern language that, in addition to the lexical and syntactical information present in lexico-syntactic rules, also makes use of semantic information. We show that the lexico-semantic patterns are superior to lexico-syntactic patterns with respect to efficiency and effectivity. When applied to event relation recognition in text-based news items in the domains of finance and politics using Hermes, an ontology-driven news personalization service, our approach has a precision and recall of approximately 80% and 70%, respectively