Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation - Bäumer, Frederik Simon; Chen, Wei-Fan; Geierhos, Michaela; Kersting, Joschka; Wachsmuth, Henning (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews - Kersting, Joschka; Geierhos, Michaela (Data Management Technologies and Applications)
We present a concept for quantifying evaluative phrases to later compare rating texts numerically instead of just relying on stars or grades. We achievethis by combining deep learning models in an aspect-based sentiment analysis pipeline along with sentiment weighting, polarity, and correlation analyses that combine deep learning results with metadata. The results provide new insights for the medical field. Our application domain, physician reviews, shows that there are millions of review texts on the Internet that cannot yet be comprehensively analyzed because previous studies have focused on explicit aspects from other domains (e.g., products). We identify, extract, and classify implicit and explicit aspect phrases equally from German-language review texts. To do so, we annotated aspect phrases representing reviews on numerous aspects of a physician, medical practice, or practice staff. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and physicians’ professions (e.g., surgeon, ophthalmologist). As a result, we have individual numerical scores that contain a variety of information based on deep learning algorithms that extract textual (evaluative) information and metadata from the Web.

Chatbot-Enhanced Requirements Resolution for Automated Service Compositions - Kersting, Joschka; Ahmed, Mobeen; Geierhos, Michaela (HCI International 2022 Posters)
This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.
Keywords: On-The-Fly Computing; Chatbot; Knowledge Base

IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING - Bäumer, Frederik Simon; Kersting, Joschka; Denisov, Sergej; Geierhos, Michaela (PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021)
Content is the new oil. Users consume billions of terabytes a day while surfing on news sites or blogs, posting on social media sites, and sending chat messages around the globe. While content is heterogeneous, the dominant form of web content is text. There are situations where more diversity needs to be introduced into text content, for example, to reuse it on websites or to allow a chatbot to base its models on the information conveyed rather than of the language used. In order to achieve this, paraphrasing techniques have been developed: One example is Text spinning, a technique that automatically paraphrases text while leaving the intent intact. This makes it easier to reuse content, or to change the language generated by the bot more human. One method for modifying texts is a combination of translation and back-translation. This paper presents NATTS, a naive approach that uses transformer-based translation models to create diversified text, combining translation steps in one model. An advantage of this approach is that it can be fine-tuned and handle technical language.
Keywords: Software Requirements; Natural Language Processing; Transfer Learning; On-The-Fly Computing

Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks - Kersting, Joschka; Geierhos, Michaela (Natural Language Processing in Artificial Intelligence -- NLPinAI 2020)
This chapter concentrates on aspect-based sentiment analysis, a form of opinion mining where algorithms detect sentiments expressed about features of products, services, etc. We especially focus on novel approaches for aspect phrase extraction and classification trained on feature-rich datasets. Here, we present two new datasets, which we gathered from the linguistically rich domain of physician reviews, as other investigations have mainly concentrated on commercial reviews and social media reviews so far. To give readers a better understanding of the underlying datasets, we describe the annotation process and inter-annotator agreement in detail. In our research, we automatically assess implicit mentions or indications of specific aspects. To do this, we propose and utilize neural network models that perform the here-defined aspect phrase extraction and classification task, achieving F1-score values of about 80% and accuracy values of more than 90%. As we apply our models to a comparatively complex domain, we obtain promising results.

Well-being in Plastic Surgery: Deep Learning Reveals Patients' Evaluations - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021))

Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting - Kersting, Joschka; Geierhos, Michaela (Natural Language Processing and Information Systems)
In this study, we describe a text processing pipeline that transforms user-generated text into structured data. To do this, we train neural and transformer-based models for aspect-based sentiment analysis. As most research deals with explicit aspects from product or service data, we extract and classify implicit and explicit aspect phrases from German-language physician review texts. Patients often rate on the basis of perceived friendliness or competence. The vocabulary is difficult, the topic sensitive, and the data user-generated. The aspect phrases come with various wordings using insertions and are not noun-based, which makes the presented case equally relevant and reality-based. To find complex, indirect aspect phrases, up-to-date deep learning approaches must be combined with supervised training data. We describe three aspect phrase datasets, one of them new, as well as a newly annotated aspect polarity dataset. Alongside this, we build an algorithm to rate the aspect phrase importance. All in all, we train eight transformers on the new raw data domain, compare 54 neural aspect extraction models and, based on this, create eight aspect polarity models for our pipeline. These models are evaluated by using Precision, Recall, and F-Score measures. Finally, we evaluate our aspect phrase importance measure algorithm.

Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P2P News Tagging - Bäumer, Frederik Simon; Kersting, Joschka; Buff, Bianca; Geierhos, Michaela (Information and Software Technologies)
Peer-to-Peer news portals allow Internet users to write news articles and make them available online to interested readers. Despite the fact that authors are free in their choice of topics, there are a number of quality characteristics that an article must meet before it is published. In addition to meaningful titles, comprehensibly written texts and meaning- ful images, relevant tags are an important criteria for the quality of such news. In this case study, we discuss the challenges and common mistakes that Peer-to-Peer reporters face when tagging news and how incorrect information can be corrected through the orchestration of existing Natu- ral Language Processing services. Lastly, we use this illustrative example to give insight into the challenges of dealing with bottom-up taxonomies.

Aspect Phrase Extraction in Sentiment Analysis with Deep Learning - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020))
This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.
Keywords: Deep Learning; Natural Language Processing; Aspect-based Sentiment Analysis

Detection of Privacy Disclosure in the Medical Domain: A Survey - Buff, Bianca; Kersting, Joschka; Geierhos, Michaela (Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020))
When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.
Keywords: Identity Disclosure; Privacy Protection; Physician Review Website; De-Anonymization; Medical Domain

Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference)

What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods)
This paper deals with online customer reviews of local multi-service providers. While many studies investigate product reviews and online labour markets with service providers delivering intangible products “over the wire”, we focus on websites where providers offer multiple distinct services that can be booked, paid and reviewed online but are performed locally offline. This type of service providers has so far been neglected in the literature. This paper analyses reviews and applies sentiment analysis. It aims to gain new insights into local multi-service providers’ performance. There is a broad literature range presented with regard to the topics addressed. The results show, among other things, that providers with good ratings continue to perform well over time. We find that many positive reviews seem to encourage sales. On average, quantitative star ratings and qualitative ratings in the form of review texts match. Further results are also achieved in this study.
Keywords: Customer Reviews; Sentiment Analysis; Online Labour Markets

Requirements Engineering in OTF-Computing - Bäumer, Frederik Simon; Geierhos, Michaela (encyclopedia.pub)
Keywords: OTF Computing; Natural Language Processing; Requirements Engineering

Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches - Bäumer, Frederik Simon; Kersting, Joschka; Geierhos, Michaela (Computers)
The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances.
Keywords: Inaccuracy Detection; Natural Language Software Requirements; Chat Bot

Potentielle Privatsphäreverletzungen aufdecken und automatisiert sichtbar machen - Bäumer, Frederik Simon; Buff, Bianca; Geierhos, Michaela (DHd 2019 Digital Humanities: multimedial & multimodal. Konferenzabstracts)

In Reviews We Trust: But Should We? Experiences with Physician Review Websites - Kersting, Joschka; Bäumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 4th International Conference on Internet of Things, Big Data and Security)
The ability to openly evaluate products, locations and services is an achievement of the Web 2.0. It has never been easier to inform oneself about the quality of products or services and possible alternatives. Forming one’s own opinion based on the impressions of other people can lead to better experiences. However, this presupposes trust in one’s fellows as well as in the quality of the review platforms. In previous work on physician reviews and the corresponding websites, it was observed that there occurs faulty behavior by some reviewers and there were noteworthy differences in the technical implementation of the portals and in the efforts of site operators to maintain high quality reviews. These experiences raise new questions regarding what trust means on review platforms, how trust arises and how easily it can be destroyed.
Keywords: Trust; Physician Reviews; Network Analysis

How to Deal with Inaccurate Service Descriptions in On-The-Fly Computing: Open Challenges - Bäumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 23rd International Conference on Natural Language and Information Systems)
The vision of On-The-Fly Computing is an automatic composition of existing software services. Based on natural language software descriptions, end users will receive compositions tailored to their needs. For this reason, the quality of the initial software service description strongly determines whether a software composition really meets the expectations of end users. In this paper, we expose open NLP challenges needed to be faced for service composition in On-The-Fly Computing.
Keywords: Requirements Extraction; Temporal Reordering of Software Functions; Inaccuracy Compensation

Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure - Kim, Yeongsu; Lee, Seungwoo; Dollmann, Markus; Geierhos, Michaela (International Journal of Advanced Science and Technology)
A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT.
Keywords: Software Engineering; Natural Language Processing; Semantic Annotation; Machine Learning; Feature Engineering; Syntactic Structure

Freiraum zur individuellen Reflexion gemeinsamer Werte - Geierhos, Michaela (Integration und Toleranz)

Rate Your Physician: Findings from a Lithuanian Physician Rating Website - Bäumer, Frederik Simon; Kersting, Joschka; Kuršelis, Vytautas; Geierhos, Michaela (Communications in Computer and Information Science)
Physician review websites are known around the world. Patients review the subjectively experienced quality of medical services supplied to them and publish an overall rating on the Internet, where quantitative grades and qualitative texts come together. On the one hand, these new possibilities reduce the imbalance of power between health care providers and patients, but on the other hand, they can also damage the usually very intimate relationship between health care providers and patients. Review websites must meet these requirements with a high level of responsibility and service quality. In this paper, we look at the situation in Lithuania: Especially, we are interested in the available possibilities of evaluation and interaction, and the quality of a particular review website measured against the available data. We thereby identify quality weaknesses and lay the foundation for future research.
Keywords: Lithuanian physician review websites; Medical service ratings

NLP in OTF Computing: Current Approaches and Open Challenges - Bäumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 24th International Conference on Information and Software Technologies (ICIST 2018))
On-The-Fly Computing is the vision of covering software needs of end users by fully-automatic compositions of existing software services. End users will receive so-called service compositions tailored to their very individual needs, based on natural language software descriptions. This everyday language may contain inaccuracies and incompleteness, which are well-known challenges in requirements engineering. In addition to existing approaches that try to automatically identify and correct these deficits, there are also new trends to involve users more in the elaboration and refinement process. In this paper, we present the relevant state of the art in the field of automated detection and compensation of multiple inaccuracies in natural language service descriptions and name open challenges needed to be tackled in NL-based software service composition.
Keywords: Inaccuracy detection; Natural language software requirements

Text Broom: A ML-based Tool to Detect and Highlight Privacy Breaches in Physician Reviews: An Insight into Our Current Work - Bäumer, Frederik Simon; Geierhos, Michaela (European Conference on Data Analysis 2018: Multidisciplinary Facets of Data Science - Book of Abstracts)

Towards a Multi-Stage Approach to Detect Privacy Breaches in Physician Reviews - Bäumer, Frederik Simon; Kersting, Joschka; Orlikowski, Matthias; Geierhos, Michaela (Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems co-located with the 14th International Conference on Semantic Systems (SEMANTiCS 2018))
Physician Review Websites allow users to evaluate their experiences with health services. As these evaluations are regularly contextualized with facts from users’ private lives, they often accidentally disclose personal information on the Web. This poses a serious threat to users’ privacy. In this paper, we report on early work in progress on “Text Broom”, a tool to detect privacy breaches in user-generated texts. For this purpose, we conceptualize a pipeline which combines methods of Natural Language Processing such as Named Entity Recognition, linguistic patterns and domain-specific Machine Learning approaches which have the potential to recognize privacy violations with wide coverage. A prototypical web application is openly accesible.
Keywords: Detection of Privacy Violations; Physician Reviews

Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-based Configuration of Text Analysis Pipelines - Bäumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 51st Hawaii International Conference on System Sciences)
Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.
Keywords: Software Product Lines: Engineering; Services; and Management; Ambiguities; Incompleteness; Natural Language Processing; Software Requirements

Unschärfe bei der Interpretation natürlichsprachlicher Anforderungsbeschreibungen - Geierhos, Michaela (Unschärfe - Der Umgang mit fehlender Eindeutigkeit)
Präzision ist kein Zufall. Sie wird vom Menschen herbeigeführt, indem Übereinstimmung mit einem Standard oder einem akzeptierten Wert angestrebt wird oder die Reproduzierbarkeit von Experimenten möglichst hoch sein muss. Was aber tun, wenn Präzision mangels verfügbarer Informationen nicht hergestellt werden kann? Wie gehen Wissenschaft und Kunst dann mit dieser fehlenden Eindeutigkeit um? Die Autorinnen und Autoren dieses Sammelbandes beleuchten aus der Perspektive ihrer jeweiligen Fachdisziplin die Chancen bei der Berücksichtigung von Unschärfe(n) in ihrer Forschung und Kunst. Denn Unschärfe ist Realität.

How to Deal with Inaccurate Service Requirements? Insights in Our Current Approach and New Ideas - Bäumer, Frederik Simon; Geierhos, Michaela (Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018))
The main idea in On-The-Fly Computing is to automatically compose existing software services according to the wishes of end-users. However, since user requirements are often ambiguous, vague and incomplete, the selection and composition of suitable software services is a challanging task. In this paper, we present our current approach to improve requirement descriptions before they are used for software composition. This procedure is fully automated, but also has limitations, for example, if necessary information is missing. In addition, and in response to the limitations, we provide insights into our above-mentioned current work that combines the existing optimization approach with a chatbot solution.

CORDULA: Software Requirements Extraction Utilizing Chatbot as Communication Interface - Friesen, Edwin; Bäumer, Frederik Simon; Geierhos, Michaela (Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018))
Natural language requirement descriptions are often unstructured, contradictory and incomplete and are therefore challenging for automatic processing. Although many of these deficits can be compensated by means of Natural Language Processing, there still remain cases where interaction with end-users is necessary for clarification. In this paper, we present our idea of using chatbot technology to establish end-user communication in order to support the automatic compensation of some deficits in natural language requirement descriptions.

Back to Basics: Extracting Software Requirements with a Syntactic Approach - Caron, Matthew; Bäumer, Frederik Simon; Geierhos, Michaela (Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018))
As our world grows in complexity, companies and employees alike need, more than ever before, solutions tailored to their exact needs. Since such tools cannot always be purchased off-the-shelf and need to be designed from the ground up, developers rely on software requirements. In this paper, we present our vision of a syntactic rule-based extraction tool for software requirements specification documents. In contrast to other methods, our tool will allow stakeholders to express their needs and wishes in unfiltered natural language, which we believe is essential for non-expert users.

Supporting the Cognitive Process in Annotation Tasks - Seemann, Nina; Geierhos, Michaela; Merten, Marie-Luis; Tophinke, Doris; Wever, Marcel Dominik; Hüllermeier, Eyke (Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft)

Using Sentiment Analysis on Local Up-to-the-Minute News: An Integrated Approach - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 23rd International Conference on Information and Software Technologies, Communications in Computer and Information Science)

Guesswork? Resolving Vagueness in User-Generated Software Requirements - Geierhos, Michaela; Baeumer , Frederik Simon (Partiality and Underspecification in Information, Languages, and Knowledge)

Studying Software Descriptions in SourceForge and App Stores for a better Understanding of real-life Requirements - Baeumer, Frederik Simon; Dollmann, Markus; Geierhos, Michaela (Proceedings of the 2nd ACM SIGSOFT International Workshop on App Market Analytics)

Internet of Things Architecture for Handling Stream Air Pollution Data - Kersting, Joschka; Geierhos, Michaela; Jung, Hanmin; Kim, Taehong (Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security)
In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.
Keywords: Wireless Sensor Network; Internet of Things; Stream Data; Air Pollution; DSMS; Real-time Data Processing

Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German - Seemann, Nina; Merten, Marie-Luis; Geierhos, Michaela; Tophinke, Doris; Hüllermeier, Eyke (Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature)
In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA – a web-based annotation tool for historical and other non-standard language data – to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.

Using Sentiment Analysis on Local Up-to-the-Minute News: An Integrated Approach - Kersting, Joschka; Geierhos, Michaela (Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania, October 12–14, 2017, Proceedings)
In this paper, we present a search solution that makes local news information easily accessible. In the era of fake news, we provide an approach for accessing news information through opinion mining. This enables users to view news on the same topics from different web sources. By applying sentiment analysis on social media posts, users can better understand how issues are captured and see people’s reactions. Therefore, we provide a local search service that first localizes news articles, then visualizes their occurrence according to the frequency of mentioned topics on a heatmap and even shows the sentiment score for each text.

Privacy Matters: Detecting Nocuous Patient Data Exposure in Online Physician Reviews - Bäumer, Frederik Simon; Grote, Nicolai; Kersting, Joschka; Geierhos, Michaela (Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania, October 12–14, 2017, Proceedings)
Consulting a physician was long regarded as an intimate and private matter. The physician-patient relationship was perceived as sensitive and trustful. Nowadays, there is a change, as medical procedures and physicians consultations are reviewed like other services on the Internet. To allay user’s privacy doubts, physician review websites assure anonymity and the protection of private data. However, there are hundreds of reviews that reveal private information and hence enable physicians or the public to identify patients. Thus, we draw attention to the cases when de-anonymization is possible. We therefore introduce an approach that highlights private information in physician reviews for users to avoid an accidental disclosure. For this reason, we combine established natural-language-processing techniques such as named entity recognition as well as handcrafted patterns to achieve a high detection accuracy. That way, we can help websites to increase privacy protection by recognizing and uncovering apparently uncritical information in user-generated texts.
Keywords: Physician Reviews; User Privacy; Nocuous Data Exposure

Guesswork? Resolving Vagueness in User-Generated Software Requirements - Geierhos, Michaela; Bäumer, Frederik Simon (Partiality and Underspecification in Information, Languages, and Knowledge)
In recent years, there has been a proliferation of technological developments that incorporate processing of human language. Hardware and software can be specialized for designated subject areas, and computational devices are designed for a widening variety of applications. At the same time, new areas and applications are emerging by demanding intelligent technology enhanced by the processing of human language. These new applications often perform tasks which handle information, and they have a capacity to reason, using both formal and human language. Many sub-areas of Artificial Intelligence demand integration of Natural Language Processing, at least to some degree. Furthermore, technologies require coverage of known as well as unknown agents, and tasks with potential variations. All of this takes place in environments with unknown factors. The book covers theoretical work, advanced applications, approaches, and techniques for computational models of information, reasoning systems, and presentation in language. The book promotes work on intelligent natural language processing and related models of information, thought, reasoning, and other cognitive processes. The topics covered by the chapters prompt further research and developments of advanced systems in the areas of logic, computability, computational linguistics, cognitive science, neuroscience of language, robotics, and artificial intelligence, among others.

Studying Software Descriptions in SourceForge and App Stores for a better Understanding of real-life Requirements - Bäumer, Frederik Simon; Dollmann, Markus; Geierhos, Michaela (Proceedings of the 2nd ACM SIGSOFT International Workshop on App Market Analytics)
Users prefer natural language software requirements because of their usability and accessibility. Many approaches exist to elaborate these requirements and to support the users during the elicitation process. But there is a lack of adequate resources, which are needed to train and evaluate approaches for requirement refinement. We are trying to close this gap by using online available software descriptions from SourceForge and app stores. Thus, we present two real-life requirements collections based on online-available software descriptions. Our goal is to show the domain-specific characteristics of content words describing functional requirements. On the one hand, we created a semantic role-labeled requirements set, which we use for requirements classification. On the other hand, we enriched software descriptions with linguistic features and dependencies to provide evidence for the context-awareness of software functionalities.

From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process - van Rooijen, Lorijn; Bäumer, Frederik Simon; Platenius, Marie Christin; Geierhos, Michaela; Hamann, Heiko; Engels, Gregor (2017 IEEE 25th International Requirements Engineering Conference Workshops (REW))
Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.
Keywords: Software; Unified modeling language; Requirements engineering; Ontologies; Search problems; Natural languages

Semantic Annotation of Software Requirements with Language Frame - Kim, Yeong-Su; Lee, Seung-Woo; Dollmann, Markus; Geierhos, Michaela (International Journal of Software Engineering for Smart Device)
An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.
Keywords: Natural Language Processing; Semantic Annotation; Machine Learning

Sentiment analysis - Geierhos, Michaela (Encyclopedia {\ "a} the business informatics)

Webmonitoring - Geierhos, Michaela (Encyclopedia {\ "a} the business informatics)

Crawler (focused / not focused) - Geierhos, Michaela (Encyclopedia {\ "a} the business informatics)

Text Mining - Geierhos, Michaela (Encyclopedia {\ "a} the business informatics)

Running Out of Words: How Similar User Stories Can Help to Elaborate Individual Natural Language Requirement Descriptions - Baeumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 22nd Conference on Information and Software Technologies (ICIST 2016))

How to Complete Customer Requirements Using Concept Expansion for Requirement Refinement - Geierhos, Michaela; Baeumer, Frederik Simon (Proceedings of the 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016))

Wie verhalten sich Aktionäre bei Unternehmenszusammenschlüssen? Modellierung sprachlicher Muster zur Analyse treibender Faktoren bei der Berichterstattung - Stotz, Sophia Charlotte; Geierhos, Michaela (DHd 2016: Modellierung - Vernetzung - Visualisierung. Die Digital Humanities als fächerübergreifendes Forschungsparadigma. Konferenzabstracts)

Wie verhalten sich Aktionäre bei Unternehmenszusammenschlüssen? Modellierung sprachlicher Muster zur Analyse treibender Faktoren bei der Berichterstattung - Stotz, Sophia; Geierhos, Michaela (DHd 2016: Modellierung - Vernetzung - Visualisierung. Die Digital Humanities als fächerübergreifendes Forschungsparadigma. Konferenzabstracts, Universität Leipzig, 7. bis 12. März 2016)
Welche Informationen über Unternehmenszusammenschlüsse werden in Zeitungsnachrichten vermittelt, und wie können diese Informationen automatisch extrahiert werden? Dies soll am Beispiel des Verhaltens von Aktionären während eines Zusammenschlusses ermittelt werden. Dazu werden die wichtigsten Aussagen über das Votum der Aktionäre im Hinblick auf eine automatische Erkennung sprachlich analysiert. Im Fokus stehen dabei die Berichte über Aktionärsabstimmungen hinsichtlich der Annahme bzw. Ablehnung eines Übernahmeangebots.

Webmonitoring - Geierhos, Michaela (Enzyklopädie der Wirtschaftsinformatik)
Keywords: Webmonitoring

Sentimentanalyse - Geierhos, Michaela (Enzyklopädie der Wirtschaftsinformatik)
Keywords: Sentimentanalyse