Computational Text Professionalization using Neural Sequence-to-Sequence Models - Mishra, Avishek

Belief-based Generation of Argumentative Claims - Alshomary, Milad; Chen, Wei-Fan; Gurcke, Timon; Wachsmuth, Henning (Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume)
When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

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.

SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH - Kersting, Joschka; Bäumer, Frederik Simon (PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020)
Keywords: Software Requirements; Natural Language Processing; Transfer Learning; On-The-Fly Computing

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)

Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets - Wever, Marcel Dominik; van Rooijen, Lorijn; Hamann, Heiko (Evolutionary Computation)
In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.

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

A Case for a New IT Ecosystem: On-The-Fly Computing - Karl, Holger; Kundisch, Dennis; Meyer auf der Heide, Friedhelm; Wehrheim, Heike (Business & Information Systems Engineering)

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity - Chen, Wei-Fan; Al-Khatib, Khalid; Wachsmuth, Henning; Stein, Benno (Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science)

Task Proposal: Abstractive Snippet Generation for Web Pages - Syed, Shahbaz; Chen, Wei-Fan; Hagen, Matthias; Stein, Benno; Wachsmuth, Henning; Potthast, Martin (Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020))

Detecting Media Bias in News Articles using Gaussian Bias Distributions - Chen, Wei-Fan; Al-Khatib, Khalid; Stein, Benno; Wachsmuth, Henning (Findings of the Association for Computational Linguistics: EMNLP 2020)

Abstractive Snippet Generation - Chen, Wei-Fan; Syed, Shahbaz; Stein, Benno; Hagen, Matthias; Potthast, Martin (Proceedings of the Web Conference 2020)

Target Inference in Argument Conclusion Generation - Alshomary, Milad; Syed, Shahbaz; Potthast, Martin; Wachsmuth, Henning (Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020))

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

Organic Programming of Dynamic Real-Time Applications - Khaluf, Lial

Requirements Engineering im OTF-Computing: Informationsextraktion und Unvollständigkeitskompensation mittels domänenspezifischer Wissensbasis - Friesen, Edwin

Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition - Chen, Wei-Fan; Al-Khatib, Khalid; Hagen, Matthias; Wachsmuth, Henning; Stein, Benno (Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom)