@misc{1153,
  author       = {{Geierhos, Michaela}},
  booktitle    = {{Enzyklopädie der Wirtschaftsinformatik}},
  editor       = {{Gronau, Norbert and Becker, Jörg and Sinz, Elmar and Suhl, Leena and Leimeister, Jan M.}},
  keywords     = {{Sentimentanalyse}},
  publisher    = {{GITO-Verlag}},
  title        = {{{Sentimentanalyse}}},
  year         = {{2016}},
}

@misc{1154,
  author       = {{Geierhos, Michaela}},
  booktitle    = {{Enzyklopädie der Wirtschaftsinformatik}},
  editor       = {{Gronau, Norbert and Becker, Jörg and Sinz, Elmar and Suhl, Leena and Leimeister, Jan M.}},
  keywords     = {{Text Mining}},
  publisher    = {{GITO-Verlag}},
  title        = {{{Text Mining}}},
  year         = {{2016}},
}

@misc{1155,
  author       = {{Geierhos, Michaela}},
  booktitle    = {{Enzyklopädie der Wirtschaftsinformatik}},
  editor       = {{Gronau, Norbert and Becker, Jörg and Sinz, Elmar and Suhl, Leena and Leimeister, Jan M.}},
  keywords     = {{Crawler}},
  publisher    = {{GITO-Verlag}},
  title        = {{{Crawler (fokussiert / nicht fokussiert)}}},
  year         = {{2016}},
}

@inproceedings{176,
  abstract     = {{Users prefer natural language software requirements because of their usability and accessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present an automated approach for identifying and semantically annotating the on-topic parts of the given descriptions. It is designed to support requirement engineers in the requirement elicitation process on detecting and analyzing requirements in user-generated content. Since no lexical resources with domain-specific information about requirements are available, we created a corpus of requirements written in controlled language by instructed users and uncontrolled language by uninstructed users. We annotated these requirements regarding predicate-argument structures, conditions, priorities, motivations and semantic roles and used this information to train classifiers for information extraction purposes. The approach achieves an accuracy of 92% for the on- and off-topic classification task and an F1-measure of 72% for the semantic annotation.}},
  author       = {{Dollmann, Markus and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)}},
  location     = {{Austin, TX, USA}},
  pages        = {{1807--1816}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{{On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements}}},
  year         = {{2016}},
}

@inproceedings{191,
  abstract     = {{One purpose of requirement refinement is that higher-level requirements have to be translated to something usable by developers. Since customer requirements are often written in natural language by end users, they lack precision, completeness and consistency. Although user stories are often used in the requirement elicitation process in order to describe the possibilities how to interact with the software, there is always something unspoken. Here, we present techniques how to automatically refine vague software descriptions. Thus, we can bridge the gap by first revising natural language utterances from higher-level to more detailed customer requirements, before functionality matters. We therefore focus on the resolution of semantically incomplete user-generated sentences (i.e. non-instantiated arguments of predicates) and provide ontology-based gap-filling suggestions how to complete unverbalized information in the user’s demand.}},
  author       = {{Geierhos, Michaela and Bäumer, Frederik Simon}},
  booktitle    = {{Proceedings of the 21st International Conference on Applications of Natural Language to Information Systems (NLDB)}},
  editor       = {{Métais, Elisabeth  and Meziane, Farid  and Saraee, Mohamad  and Sugumaran, Vijayan  and Vadera, Sunil }},
  isbn         = {{978-3-319-41753-0}},
  keywords     = {{Requirement refinement, Concept expansion, Ontology-based instantiation of predicate-argument structure}},
  location     = {{Salford, UK}},
  pages        = {{37--47}},
  publisher    = {{Springer}},
  title        = {{{How to Complete Customer Requirements: Using Concept Expansion for Requirement Refinement}}},
  doi          = {{10.1007/978-3-319-41754-7_4}},
  volume       = {{9612}},
  year         = {{2016}},
}

@inproceedings{158,
  abstract     = {{While requirements focus on how the user interacts with the system, user stories concentrate on the purpose of software features. But in practice, functional requirements are also described in user stories. For this reason, requirements clarification is needed, especially when they are written in natural language and do not stick to any templates (e.g., "as an X, I want Y so that Z ..."). However, there is a lot of implicit knowledge that is not expressed in words. As a result, natural language requirements descriptions may suffer from incompleteness. Existing approaches try to formalize natural language or focus only on entirely missing and not on deficient requirements. In this paper, we therefore present an approach to detect knowledge gaps in user-generated software requirements for interactive requirement clarification: We provide tailored suggestions to the users in order to get more precise descriptions. For this purpose, we identify not fully instantiated predicate argument structures in requirements written in natural language and use context information to realize what was meant by the user.}},
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 22nd International Conference on Information and Software Technologies (ICIST)}},
  editor       = {{Dregvaite, Giedre  and Damasevicius, Robertas }},
  isbn         = {{978-3-319-46253-0}},
  keywords     = {{Natural language requirements clarification, Syntactically incomplete requirements, Compensatory user stories}},
  location     = {{Druskininkai, Lithuania}},
  pages        = {{549--558}},
  publisher    = {{Springer}},
  title        = {{{Running out of Words: How Similar User Stories Can Help to Elaborate Individual Natural Language Requirement Descriptions}}},
  doi          = {{10.1007/978-3-319-46254-7_44}},
  volume       = {{639}},
  year         = {{2016}},
}

@inbook{25408,
  author       = {{Baeumer, Frederik Simon and Geierhos, Michaela and Schulze, Sabine}},
  booktitle    = {{Information and Software Technologies: 21th International Conference, ICIST 2015, Druskininkai, Lithuania, October 15-16, 2015, Proceedings}},
  isbn         = {{978-3-319-24769-4 }},
  pages        = {{3--15}},
  publisher    = {{Springer International Publishing}},
  title        = {{{A System for Uncovering Latent Connectivity of Health Care Providers in Online Reviews}}},
  volume       = {{538}},
  year         = {{2015}},
}

@inproceedings{25409,
  author       = {{Baeumer, Frederik Simon and Dollmann, Markus and Geierhos, Michaela}},
  issn         = {{1877-0509}},
  pages        = {{417--424}},
  publisher    = {{Elsevier}},
  title        = {{{Find a Physician by Matching Medical Needs described in your Own Words}}},
  volume       = {{63}},
  year         = {{2015}},
}

@inbook{25413,
  author       = {{Geierhos, Michaela and  Baeumer,  Frederik Simon  and  Sabine, Schulze and Valentina, Stu{\ss}}},
  booktitle    = {{Current Approaches in Applied Artificial Intelligence, Lecture Notes in Computer Science}},
  editor       = {{Ali, Moonis and Kwon, Young Sig  and Lee, Chang-Hwan  and Kim, Juntae and  Kim, Yongdai}},
  isbn         = {{978-3-319-19065-5}},
  pages        = {{305--315}},
  publisher    = {{Springer International Publishing Switzerland}},
  title        = {{{Filtering Reviews by Random Individual Error}}},
  volume       = {{9101}},
  year         = {{2015}},
}

@inproceedings{25414,
  author       = {{Geierhos, Michaela and Baeumer,  Frederik Simon  and Schulze, Sabine and Stu{\ss},  Valentina}},
  booktitle    = {{ECIS 2015 Completed Research Papers}},
  title        = {{{I grade what I get but write what I think." Inconsistency Analysis in Patients‘ Reviews}}},
  year         = {{2015}},
}

@inproceedings{25587,
  author       = {{Geierhos, Michaela and Stu{\ss}, Valentina}},
  pages        = {{239--243}},
  title        = {{{Identifikation kognitiver Effekte in Online-Bewertungen}}},
  year         = {{2015}},
}

@inproceedings{25588,
  author       = {{Geierhos, Michaela and Baeumer, Frederik Simon}},
  pages        = {{69--72}},
  title        = {{{Second-hand experience reports: Findings {\ "about the authorship of doctor reviews in online portals}}},
  year         = {{2015}},
}

@inproceedings{25589,
  author       = {{Geierhos, Michaela and  Schulze, Sabine and Baeumer,  Frederik Simon}},
  booktitle    = {{Proceedings of the 7th International Conference on Agents and Artificial Intelligence}},
  isbn         = {{ 978-989-758-073-4}},
  pages        = {{277--283}},
  title        = {{{What did you mean? Facing the Challenges of User-generated Software Requirements}}},
  year         = {{2015}},
}

@article{25593,
  author       = {{Geierhos, Michaela and Schulze, Sabine}},
  journal      = {{ForschungsForum Paderborn}},
  pages        = {{14--19}},
  title        = {{{The satisfied patient 2.0: Analysis of anonymous doctor reviews to generate a patient mood}}},
  volume       = {{18}},
  year         = {{2015}},
}

@article{25594,
  author       = {{Geierhos, Michaela and Schulze, Sabine}},
  journal      = {{ForschungsForum Paderborn}},
  pages        = {{14--19}},
  title        = {{{The Satisfied Patient 2.0: Analysis of anonymous doctor ratings to gain insight into patient sentimen}}},
  volume       = {{18}},
  year         = {{2015}},
}

@inbook{293,
  abstract     = {{Opinion mining from physician rating websites depends on the quality of the extracted information. Sometimes reviews are user-error prone and the assigned stars or grades contradict the associated content. We therefore aim at detecting random individual error within reviews. Such errors comprise the disagreement in polarity of review texts and the respective ratings. The challenges that thereby arise are (1) the content and sentiment analysis of the review texts and (2) the removal of the random individual errors contained therein. To solve these tasks, we assign polarities to automatically recognized opinion phrases in reviews and then check for divergence in rating and text polarity. The novelty of our approach is that we improve user-generated data quality by excluding error-prone reviews on German physician websites from average ratings.}},
  author       = {{Geierhos, Michaela and Bäumer, Frederik Simon and Schulze, Sabine and Stuß, Valentina}},
  booktitle    = {{Proceedings of the 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2015)}},
  editor       = {{Ali, Moonis  and Kwon, Young Sig and Lee, Chang-Hwan and Kim, Juntae  and Kim, Yongdai }},
  isbn         = {{978-3-319-19065-5}},
  location     = {{Seoul, South Korea}},
  pages        = {{305--315}},
  publisher    = {{Springer}},
  title        = {{{Filtering Reviews by Random Individual Error}}},
  doi          = {{10.1007/978-3-319-19066-2_30}},
  volume       = {{9101}},
  year         = {{2015}},
}

@inproceedings{1141,
  author       = {{Stuß, Valentina and Geierhos, Michaela}},
  booktitle    = {{DHd 2015: Book of Abstracts}},
  location     = {{Graz, Austria}},
  pages        = {{239--243}},
  publisher    = {{ZIM-ACDH}},
  title        = {{{Identifikation kognitiver Effekte in Online-Bewertungen}}},
  year         = {{2015}},
}

@inproceedings{1142,
  author       = {{Geierhos, Michaela and Bäumer, Frederik Simon}},
  booktitle    = {{DHd 2015: Book of Abstracts}},
  location     = {{Graz, Austria}},
  pages        = {{69--72}},
  publisher    = {{ZIM-ACDH}},
  title        = {{{Erfahrungsberichte aus zweiter Hand: Erkenntnisse über die Autorschaft von Arztbewertungen in Online-Portalen}}},
  year         = {{2015}},
}

@article{1143,
  abstract     = {{Der Erfahrungsaustausch zwischen Patienten findet heutzutage zunehmend im Internet statt. Bewertungsportale wie jameda, DocInsider oder imedo.de bieten Patienten und deren Angehörigen die Möglichkeit, anonym Beschwerden zu äußern oder Weiterempfehlungen auszusprechen. Gleichzeitig ermöglichen diese hunderttausend Individualerfahrungen die Erhebung der Patientenzufriedenheit sowie die Überprüfung bestehender Gerüchte, wie z. B. dass Privatpatienten schneller einen Arzttermin bekommen und weniger Zeit im Wartezimmer verbringen. Die Analyse anonymer Online-Arztbewertungen kann nur dann erfolgreich sein, wenn bei der Interpretation der Patientenerfahrungsberichte berücksichtigt wird, dass behandlungsqualitätsunabhängige Faktoren Auswirkungen auf die subjektive Bewertung und das Beschwerdeverhalten haben. Ein neuer Ansatz ist daher, bedeutende Indikatoren für die Patientenzufriedenheit im Web 2.0 zur Generierung eines detaillierten Erfahrungs- und Patientenstimmungsbildes unter Berücksichtigung demographischer und regionaler Einflüsse zu ermitteln.}},
  author       = {{Geierhos, Michaela and Schulze, Sabine}},
  journal      = {{ForschungsForum Paderborn}},
  pages        = {{14--19}},
  publisher    = {{Universität Paderbon}},
  title        = {{{Der zufriedene Patient 2.0: Analyse anonymer Arztbewertungen zur Generierung eines Patientenstimmungsbildes}}},
  volume       = {{18}},
  year         = {{2015}},
}

@inproceedings{1144,
  abstract     = {{Adopting the concept of “Local Grammars” (M. Gross), which were successfully applied in practice by (Geierhos, 2010) to biographical information extraction in English our project aims to detect, encode, and finally visualize relations between persons. Our corpus consists of the digitised biographical lexicon “Neue Deutsche Biographie (NDB)”, roughly 21.000 biographies in 25 volumes in print since 1953. We developed local grammars and suitable dictionaries to describe interpersonal relations and applied them to the corpus with Unitex 3.1. The local grammars were designed to integrate existing TEI-XML structures in the corpus. Using the ability of local grammars in Unitex to act as transducers we were able to produce XML-Tags and encode semantic information. Based on grammars for personal names and places we described interpersonal relations like to study, predecessors and successors as well as friends and circles. Afterwards we
identified persons (as given in the authority file or index). Finally we displayed relations on our website in an interactive and dynamic way. Utilizing the Javascript library D3.js we represented named relations between identified individuals as ego centred network graphs.}},
  author       = {{Stotz, Sophia and Stuß, Valentina and Reinert , Matthias and Schrott, Maximilian}},
  booktitle    = {{Proceedings of the First Conference on Biographical Data in a Digital World 2015}},
  editor       = {{ter Braake, Serge and Fokkens, Antske and Sluijter, Ronald and Declerck, Thierry and Wandl-Vogt, Eveline}},
  issn         = {{16130073}},
  keywords     = {{Local Grammar, Relation Extraction, Visualisation}},
  location     = {{Amsterdam, Netherlands}},
  pages        = {{74--80}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Interpersonal relations in biographical dictionaries. A case study}}},
  volume       = {{1399}},
  year         = {{2015}},
}