Using Geolocated Text to Quantify Location in Real Estate Appraisal - Heuwinkel, Tim; Kucklick, Jan-Peter; Müller, Oliver (55th Annual Hawaii International Conference on System Sciences (HICSS-55))
Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.
Keywords: Real Estate Appraisal; Text Regression; Natural Language Processing (NLP); Location Intelligence; Wikipedia

Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis - Kersting, Joschka; Bäumer, Frederik Simon (Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications)
This paper aims at discussing past limitations set in sentiment analysis research regarding explicit and implicit mentions of opinions. Previous studies have regularly neglected this question in favor of methodical research on standard-datasets. Furthermore, they were limited to linguistically less-diverse domains, such as commercial product reviews. We face this issue by annotating a German-language physician review dataset that contains numerous implicit, long, and complex statements that indicate aspect ratings, such as the physician’s friendliness. We discuss the nature of implicit statements and present various samples to illustrate the challenge described.
Keywords: Sentiment analysis; Natural language processing; Aspect phrase extraction

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

Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach - Prester, Julian; Wagner, Gerit; Schryen, Guido; Hassan, Nik Rushdi (Decision Support Systems)
Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.
Keywords: Ideational impact; citation classification; academic recommender systems; natural language processing; deep learning; cumulative tradition

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

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

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

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

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