TY - CHAP AU - Bäumer, Frederik Simon AU - Chen, Wei-Fan AU - Geierhos, Michaela AU - Kersting, Joschka AU - Wachsmuth, Henning ED - Haake, Claus-Jochen ED - Meyer auf der Heide, Friedhelm ED - Platzner, Marco ED - Wachsmuth, Henning ED - Wehrheim, Heike ID - 45882 T2 - On-The-Fly Computing -- Individualized IT-services in dynamic markets TI - Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation VL - 412 ER - TY - CHAP AB - 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. AU - Kersting, Joschka AU - Geierhos, Michaela ED - Cuzzocrea, Alfredo ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Quix, Christoph ID - 46205 SN - 1865-0929 T2 - Data Management Technologies and Applications TI - Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews VL - 1860 ER - TY - CHAP AB - 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. AU - Kersting, Joschka AU - Ahmed, Mobeen AU - Geierhos, Michaela ED - Stephanidis, Constantine ED - Antona, Margherita ED - Ntoa, Stavroula ID - 32179 KW - On-The-Fly Computing KW - Chatbot KW - Knowledge Base SN - 1865-0929 T2 - HCI International 2022 Posters TI - Chatbot-Enhanced Requirements Resolution for Automated Service Compositions VL - 1580 ER - TY - CONF AB - 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. AU - Bäumer, Frederik Simon AU - Kersting, Joschka AU - Denisov, Sergej AU - Geierhos, Michaela ID - 26049 KW - Software Requirements KW - Natural Language Processing KW - Transfer Learning KW - On-The-Fly Computing T2 - PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021 TI - IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING ER - TY - CHAP AB - 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. AU - Kersting, Joschka AU - Geierhos, Michaela ED - Loukanova, Roussanka ID - 17905 T2 - Natural Language Processing in Artificial Intelligence -- NLPinAI 2020 TI - Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks VL - 939 ER - TY - CONF AU - Kersting, Joschka AU - Geierhos, Michaela ID - 22051 T2 - Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021) TI - Well-being in Plastic Surgery: Deep Learning Reveals Patients' Evaluations ER - TY - CHAP AB - 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. AU - Kersting, Joschka AU - Geierhos, Michaela ED - Kapetanios, Epaminondas ED - Horacek, Helmut ED - Métais, Elisabeth ED - Meziane, Farid ID - 22052 T2 - Natural Language Processing and Information Systems TI - Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting VL - 12801 ER - TY - CHAP AB - 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. AU - Bäumer, Frederik Simon AU - Kersting, Joschka AU - Buff, Bianca AU - Geierhos, Michaela ED - Audrius, Lopata ED - Rita, Butkienė ED - Daina, Gudonienė ED - Vilma, Sukackė ID - 17347 T2 - Information and Software Technologies TI - Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P2P News Tagging VL - 1283 ER - TY - CONF AB - 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. AU - Kersting, Joschka AU - Geierhos, Michaela ID - 15580 KW - Deep Learning KW - Natural Language Processing KW - Aspect-based Sentiment Analysis T2 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020) TI - Aspect Phrase Extraction in Sentiment Analysis with Deep Learning ER - TY - CONF AB - 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. AU - Buff, Bianca AU - Kersting, Joschka AU - Geierhos, Michaela ID - 15582 KW - Identity Disclosure KW - Privacy Protection KW - Physician Review Website KW - De-Anonymization KW - Medical Domain T2 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020) TI - Detection of Privacy Disclosure in the Medical Domain: A Survey ER - TY - CONF AU - Kersting, Joschka AU - Geierhos, Michaela ID - 15635 T2 - Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference TI - Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis ER - TY - CONF AB - 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. AU - Kersting, Joschka AU - Geierhos, Michaela ID - 15256 KW - Customer Reviews KW - Sentiment Analysis KW - Online Labour Markets T2 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods TI - What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers ER - TY - GEN AU - Bäumer, Frederik Simon AU - Geierhos, Michaela ID - 8312 KW - OTF Computing KW - Natural Language Processing KW - Requirements Engineering T2 - encyclopedia.pub TI - Requirements Engineering in OTF-Computing ER - TY - JOUR AB - 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. AU - Bäumer, Frederik Simon AU - Kersting, Joschka AU - Geierhos, Michaela ID - 8424 IS - 1 JF - Computers KW - Inaccuracy Detection KW - Natural Language Software Requirements KW - Chat Bot SN - 2073-431X TI - Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches VL - 8 ER - TY - GEN AU - Bäumer, Frederik Simon AU - Buff, Bianca AU - Geierhos, Michaela ED - Sahle, Patrick ID - 8532 SN - 978-3-00-062166-6 T2 - DHd 2019 Digital Humanities: multimedial & multimodal. Konferenzabstracts TI - Potentielle Privatsphäreverletzungen aufdecken und automatisiert sichtbar machen ER - TY - CONF AB - 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. AU - Kersting, Joschka AU - Bäumer, Frederik Simon AU - Geierhos, Michaela ED - Ramachandran, Muthu ED - Walters, Robert ED - Wills, Gary ED - Méndez Muñoz, Víctor ED - Chang, Victor ID - 9613 KW - Trust KW - Physician Reviews KW - Network Analysis SN - 978-989-758-369-8 T2 - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security TI - In Reviews We Trust: But Should We? Experiences with Physician Review Websites ER - TY - CHAP AB - 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. AU - Bäumer, Frederik Simon AU - Geierhos, Michaela ED - Silberztein, Max ED - Atigui, Faten ED - Kornyshova, Elena ED - Métais, Elisabeth ED - Meziane, Farid ID - 2322 KW - Requirements Extraction KW - Temporal Reordering of Software Functions KW - Inaccuracy Compensation SN - 978-3-319-91946-1 T2 - Proceedings of the 23rd International Conference on Natural Language and Information Systems TI - How to Deal with Inaccurate Service Descriptions in On-The-Fly Computing: Open Challenges VL - 10859 ER - TY - JOUR AB - 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. AU - Kim, Yeongsu AU - Lee, Seungwoo AU - Dollmann, Markus AU - Geierhos, Michaela ID - 2331 JF - International Journal of Advanced Science and Technology KW - Software Engineering KW - Natural Language Processing KW - Semantic Annotation KW - Machine Learning KW - Feature Engineering KW - Syntactic Structure SN - 2005-4238 TI - Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure VL - 112 ER - TY - CHAP AU - Geierhos, Michaela ED - Schnebel, Karin B. ED - Taubenböck, Andrea ID - 6436 SN - 978-3-86281-135-9 T2 - Integration und Toleranz TI - Freiraum zur individuellen Reflexion gemeinsamer Werte ER - TY - CHAP AB - 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. AU - Bäumer, Frederik Simon AU - Kersting, Joschka AU - Kuršelis, Vytautas AU - Geierhos, Michaela ED - Damaševičius, Robertas ED - Vasiljevienė, Giedrė ID - 4338 KW - Lithuanian physician review websites KW - Medical service ratings SN - 1865-0929 T2 - Communications in Computer and Information Science TI - Rate Your Physician: Findings from a Lithuanian Physician Rating Website VL - 920 ER -