@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{9230, author = {{Mildorf, Jarmila}}, booktitle = {{Narrative Theory, Literature, and New Media: Narrative Minds and Virtual Worlds}}, editor = {{Hatavara, Mari and Hyvärinen, Matti and Mäkälä, Maria and Mäyrä, Frans}}, pages = {{256--277}}, publisher = {{Routledge}}, title = {{{Performing Selves and Audience Design: Interview Narratives on the Internet}}}, year = {{2016}}, } @inbook{9227, author = {{Mildorf, Jarmila}}, booktitle = {{Pragmatic Perspectives on Postcolonial Discourse: Linguistics and Literature}}, editor = {{Schubert, Christoph and Volkmann, Laurenz}}, pages = {{99--113}}, publisher = {{Cambridge Scholars}}, title = {{{Pragmatic Implications of ‘You’-Narration for Postcolonial Fiction: Mohsin Hamid’s "How to Get Filthy Rich in Rising Asia"}}}, year = {{2016}}, } @inbook{9229, author = {{Mildorf, Jarmila and Kinzel, Till}}, booktitle = {{Audionarratology: Interfaces of Sound and Narrative}}, editor = {{Mildorf, Jarmila and Kinzel, Till}}, pages = {{1--26}}, publisher = {{de Gruyter}}, title = {{{Audionarratology: Prolegomena to a Research Paradigm Exploring Sound and Narrative.” }}}, year = {{2016}}, } @inbook{9228, author = {{Mildorf, Jarmila}}, booktitle = {{Audionarratology: Interfaces of Sound and Narrative}}, editor = {{Mildorf, Jarmila and Kinzel, Till}}, pages = {{239--256}}, publisher = {{de Gruyter}}, title = {{{Pictures into Sound: Aural World-Making in Art Gallery Audio Guides}}}, year = {{2016}}, } @article{9209, author = {{Mildorf, Jarmila}}, journal = {{International Journal of Literary Linguistics}}, number = {{2}}, pages = {{1--25}}, title = {{{Constructing Dialogues, (Re)constructing the Past: ‘Remembered’ Conversations in Frank McCourt’s "Angela’s Ashes"}}}, volume = {{5}}, year = {{2016}}, } @article{9208, author = {{Mildorf, Jarmila}}, journal = {{Language and Literature}}, number = {{2}}, pages = {{145--158}}, title = {{{Reconsidering Second-Person Narration and Involvement}}}, volume = {{25}}, year = {{2016}}, } @book{9194, editor = {{Kinzel, Till and Mildorf, Jarmila}}, isbn = {{9783110464320}}, pages = {{268}}, publisher = {{de Gruyter}}, title = {{{Audionarratology: Interfaces of Sound and Narrative}}}, volume = {{52}}, year = {{2016}}, } @article{9207, author = {{Mildorf, Jarmila and Kinzel, Till}}, journal = {{CounterText}}, number = {{3}}, pages = {{307--321}}, title = {{{Multisensory Imaginings: An Audionarratological Analysis of Philip Roth’s Novel Indignation and its German Radio Play Adaptation Empörung}}}, volume = {{2}}, year = {{2016}}, } @book{35501, editor = {{Ehland, Christoph and Wächter, Cornelia}}, isbn = {{9789004313361}}, pages = {{273}}, publisher = {{Brill}}, title = {{{Middlebrow and Gender: 1890-1945}}}, volume = {{Volume 62}}, year = {{2016}}, } @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}}, } @inproceedings{1145, abstract = {{Received medical services are increasingly discussed and recommended on physician rating websites (PRWs). The reviews and ratings on these platforms are valuable sources of information for patient opinion mining. In this paper, we have tackled three issues that come along with inconsistency analysis on PRWs: (1) Natural language processing of user-generated reviews, (2) the disagreement in polarity of review text and its corresponding numerical ratings (individual inconsistency) and (3) the differences in patients’ rating behavior for the same service category (e.g. ‘treatment’) expressed by varying grades on the entire data set (collective inconsistency). Thus, the basic idea is first to identify relevant opinion phrases that describe service categories and to determine their polarity. Subsequently, the particular phrase has to be assigned to its corresponding numerical rating category before checking the (dis-)agreement of polarity values. For this purpose, several local grammars for the pattern-based analysis as well as domain-specific dictionaries for the recognition of entities, aspects and polarity were applied on 593,633 physician reviews from both German PRWs jameda.de and docinsider.de. Furthermore, our research contributes to content quality improvement of PRWs because we provide a technique to detect inconsistent reviews that could be ignored for the computation of average ratings.}}, author = {{Geierhos, Michaela and Bäumer, Frederik Simon and Schulze, Sabine and Stuß, Valentina}}, booktitle = {{ECIS 2015 Completed Research Papers}}, isbn = {{9783000502842}}, location = {{Münster, Germany}}, publisher = {{Elsevier}}, title = {{{"I grade what I get but write what I think." Inconsistency Analysis in Patients' Reviews}}}, doi = {{10.18151/7217324}}, year = {{2015}}, } @techreport{1147, abstract = {{Der Erfahrungsaustausch zwischen Patienten findet verstärkt über Arztbewertungsportale statt. Dabei ermöglicht die Anonymität des Netzes ein weitestgehend ehrliches Beschwerdeverhalten, von dem das sensible Arzt-Patienten-Vertrauensverhältnis unbeschädigt bleibt. Im Rahmen des vorliegenden Beitrags wurden anonyme Arztbewertungen im Web 2.0 automatisiert ausgewertet, um Einflussfaktoren auf das Beschwerdeverhalten deutscher Patienten zu bestimmen und in der Gesellschaft vermeintlich etablierte „Patienten-Mythen“ aufzuklären. Die Aufdeckung von Irrtümern und Zufriedenheitsindikatoren soll längerfristig dazu dienen, Patientenäußerungen differenzierter zu interpretieren und somit zu einer nachhaltigen Verbesserung der Arzt-Patienten-Beziehung beizutragen.}}, author = {{Geierhos, Michaela and Schulze, Sabine and Bäumer, Frederik Simon}}, pages = {{18}}, publisher = {{Verbraucherzentrale NRW/Kompetenzzentrum Verbraucherforschung NRW}}, title = {{{Der zufriedene Patient 2.0: Analyse anonymer Arztbewertungen im Web 2.0}}}, doi = {{10.15501/kvfwp_3}}, volume = {{3}}, year = {{2015}}, } @inproceedings{1148, abstract = {{The individual search for information about physicians on Web 2.0 platforms can affect almost all aspects of our lives. People can directly access physician rating websites via web browsers or use any search engine to find physician reviews and ratings filtered by location resp. specialty. However, sometimes keyword search does not meet user needs because of the disagreement of users’ common terms queries for symptoms and the widespread medical terminology. In this paper, we present the prototype of a specialised search engine that overcomes this by indexing user-generated content (i.e., review texts) for physician discovery and provides automatic suggestions as well as an appropriate visualisation. On the one hand, we consider the available numeric physician ratings as sorting criterion for the ranking of query results. Furthermore, we extended existing ranking algorithms with respect to domain-specific types and physicians ratings on the other hand. We gathered more than 860,000 review texts and collected more than 213,000 physician records. A random test shows that about 19.7% of 5,100 different words in total are health- related and partly belong to consumer health vocabularies. Our evaluation results show that the query results fit user's particular health issues when seeking for physicians.}}, author = {{Bäumer, Frederik Simon and Dollmann, Markus and Geierhos, Michaela}}, booktitle = {{The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2015) / The 5th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2015) / Affiliated Workshops}}, editor = {{Shakshuki, Elhadi M.}}, issn = {{18770509}}, keywords = {{Physician Discovery, Consumer Health Vocabulary, Common Terms Query}}, location = {{Berlin, Germany}}, pages = {{417--424}}, publisher = {{Elsevier}}, title = {{{Find a Physician by Matching Medical Needs described in your Own Words}}}, doi = {{10.1016/j.procs.2015.08.362}}, volume = {{63}}, year = {{2015}}, } @inbook{1149, abstract = {{The contacts a health care provider (HCP), like a physician, has to other HCPs is perceived as a quality characteristic by patients. So far, only the German physician rating website jameda.de gives information about the interconnectedness of HCPs in business networks. However, this network has to be maintained manually and is thus incomplete. We therefore developed a system for uncovering latent connectivity of HCPs in online reviews to provide users with more valuable information about their HCPs. The overall goal of this approach is to extend already existing business networks of HCPs by integrating connections that are newly discovered by our system. Our most recent evaluation results are promising: 70.8 % of the connections extracted from the reviews texts were correctly identified and in total 3,788 relations were recognized that have not been displayed in jameda.de’s network before.}}, author = {{Bäumer, Frederik Simon and Geierhos, Michaela and Schulze, Sabine}}, booktitle = {{Information and Software Technologies. 21st International Conference, ICIST 2015, Druskininkai, Lithuania, October 15-16, 2015. Proceedings}}, editor = {{Dregvaite, Giedre and Damasevicius, Robertas}}, isbn = {{978-3-319-24769-4}}, keywords = {{Latent Connectivity, Person Named Entity Recognition and Disambiguation, Health Care Provider Reviews}}, location = {{Druskininkai, Lithuania}}, pages = {{3--15}}, publisher = {{Springer}}, title = {{{A System for Uncovering Latent Connectivity of Health Care Providers in Online Reviews}}}, doi = {{10.1007/978-3-319-24770-0_1}}, volume = {{538}}, year = {{2015}}, } @inbook{1150, abstract = {{Patients 2.0 increasingly inform themselves about the quality of medical services on physician rating websites. However, little is known about whether the reviews and ratings on these websites truly reflect the quality of services or whether the ratings on these websites are rather influenced by patients’ individual rating behavior. Therefore, we investigate more than 790,000 physician reviews on Germany’s most used physician rating website jameda.de. Our results show that patients’ ratings do not only reflect treatment quality but are also influenced by treatment quality independent factors like age and complaint behavior. Hence, we provide evidence that users should be well aware of user specific rating distortions when intending to make their physician choice based on these ratings.}}, author = {{Geierhos, Michaela and Bäumer, Frederik Simon and Schulze, Sabine and Klotz, Caterina}}, booktitle = {{Modeling and Using Context. 9th International and Interdisciplinary Conference, CONTEXT 2015, Lanarca, Cyprus, November 2-6, 2015. Proceedings}}, editor = {{Christiansen, Henning and Stojanovic, Isidora and Papadopoulos, George A.}}, isbn = {{9783319255903}}, keywords = {{Health 2.0, Rating Behavior, Patient Opinion Mining on Physician Rating Websites}}, location = {{Larnaca, Cyprus}}, pages = {{159--171}}, publisher = {{Springer}}, title = {{{Understanding the Patient 2.0: Gaining Insight into Patients' Rating Behavior by User-generated Physician Review Mining}}}, doi = {{10.1007/978-3-319-25591-0_12}}, volume = {{9405}}, year = {{2015}}, }