Getting Around to It: How Design Science Researchers Set Future Work Agendas

Background: There is a long tradition of writing about future work in research papers, and information systems design science research (IS DSR) is no exception. However, there is a lack of studies on (1) how IS DSR authors currently envision the next steps for their work and (2) guidelines to improve the communication of opportunities to accumulate knowledge. Method: This paper contributes to this topic, building on a systematic literature review of 123 IS DSR papers published between 2018 and 2022. Results: Design-oriented research requires the research team to decide which tasks to carry out immediately in building the future and which to postpone as research debt. The paper\u27s contribution is threefold. First, we propose a research debt lifecycle to support (1) project stakeholders, (2) IS DSR community, and (3) societies looking for better futures. Second, we discuss the anatomy of future work in recent IS DSR. Finally, we suggest guidelines to manage and report the next research steps. Conclusion: This paper presents a pioneering assessment of future work suggestions in the IS field, focusing on the design science research paradigm. Future work directions emerge from researchers\u27 choices during the IS DSR process that must be continuously managed

Analisis Pembangunan Korpus Berpasangan Untuk Pembangkitan Parafrasa Pada Makalah Ilmiah

Pembangunan mesin yang dapat membangkitkan kalimat baru dengan tingkat semantik yang tinggi namun secara penulisan berbeda (parafrasa) membutuhkan sumberdaya bahasa berupa korpus parallel. Proses pembangunan korpus memerlukan analisis awal sesuai dengan domain dari mesin yang akan dibuat. Pada penelitian ini dilakukan analis dalam pembangunan korpus berpasangan pada makalah ilmiah. Kalimat-kalimat pada makalah ilmiah memiliki karakteristik yang berbeda dengan domain lain seperti berita atau media sosial. Dari hasil proses ekstraksi awal didapatkan 590.402 kalimat isi  dan 23.584 kalimat abstrak. Hasil dari penelitian ini dapat menjadi kandidat korpus yang dilakukan dengan proses terkomputerisasi.Pembangunan mesin yang dapat membangkitkan kalimat baru dengan tingkat semantik yang tinggi namun secara penulisan berbeda (parafrasa) membutuhkan sumberdaya bahasa berupa korpus parallel. Proses pembangunan korpus memerlukan analisis awal sesuai dengan domain dari mesin yang akan dibuat. Pada penelitian ini dilakukan analis dalam pembangunan korpus berpasangan pada makalah ilmiah. Kalimat-kalimat pada makalah ilmiah memiliki karakteristik yang berbeda dengan domain lain seperti berita atau media sosial. Dari hasil proses ekstraksi awal didapatkan 590.402 kalimat isi  dan 23.584 kalimat abstrak. Hasil dari penelitian ini dapat menjadi kandidat korpus yang dilakukan dengan proses terkomputerisasi

Diversity in IS research : a fictive metaphor analysis

In striving to understand Information Systems phenomena Information Systems researchers frequently draw on a seemingly endless array of different disciplines to inform their studies. This act has drawn both the ire and admiration of those within the field as well as those outside its porous boundaries. On the one hand Information Systems researchers are berated for being chaotic and schizophrenic in their combined research endeavour - for producing a collective output that shows neither rhyme nor reason. On the other hand they are praised for being intellectually open and democratic in their approach. These reactions draw their strength from the many issues that stem from diversity in Information Systems research. These reactions are stimulated in part by the assertion that research in the Information Systems discipline is diverse. Despite this assertion not much is known or understood about diversity in Information Systems research. This thesis addresses this critical oversight by making research diversity the prime focus. The contributions it makes to current understandings of research diversity in Information Systems are philosophical, theoretical and empirical. Philosophically, this thesis relies on the novel approach of fictism - a blend of positivism and interpretivism. Theoretically, it explores diversity through the alternative lens of concepts. Empirically it examines the conceptual diversity of three key Information Systems concepts: organisations, technology and people. Grounded in Lakoff and Johnson's (1980) work with metaphors, the results show that Information Systems research may not be as diverse as was initially thought. Of the three primary views of key Information Systems concepts - machine, organism and culture - the study finds a distinct bias toward conceptualising these concepts as machines. This bias, one that exists at the very core of the Information Systems research endeavour, has important implications not only for individual researchers but the broader Information Systems community alike

Language representations for computational argumentation

Argumentation is an essential feature and, arguably, one of the most exciting phenomena of natural language use. Accordingly, it has fascinated scholars and researchers in various fields, such as linguistics and philosophy, for long. Its computational analysis, falling under the notion of computational argumentation, is useful in a variety of domains of text for a range of applications. For instance, it can help to understand users’ stances in online discussion forums towards certain controversies, to provide targeted feedback to users for argumentative writing support, and to automatically summarize scientific publications. As in all natural language processing pipelines, the text we would like to analyze has to be introduced to computational argumentation models in the form of numeric features. Choosing such suitable semantic representations is considered a core challenge in natural language processing. In this context, research employing static and contextualized pretrained text embedding models has recently shown to reach state-of-the-art performances for a range of natural language processing tasks. However, previous work has noted the specific difficulty of computational argumentation scenarios with language representations as one of the main bottlenecks and called for targeted research on the intersection of the two fields. Still, the efforts focusing on the interplay between computational argumentation and representation learning have been few and far apart. This is despite (a) the fast-growing body of work in both computational argumentation and representation learning in general and (b) the fact that some of the open challenges are well known in the natural language processing community. In this thesis, we address this research gap and acknowledge the specific importance of research on the intersection of representation learning and computational argumentation. To this end, we (1) identify a series of challenges driven by inherent characteristics of argumentation in natural language and (2) present new analyses, corpora, and methods to address and mitigate each of the identified issues. Concretely, we focus on five main challenges pertaining to the current state-of-the-art in computational argumentation: (C1) External knowledge: static and contextualized language representations encode distributional knowledge only. We propose two approaches to complement this knowledge with knowledge from external resources. First, we inject lexico-semantic knowledge through an additional prediction objective in the pretraining stage. In a second study, we demonstrate how to inject conceptual knowledge post hoc employing the adapter framework. We show the effectiveness of these approaches on general natural language understanding and argumentative reasoning tasks. (C2) Domain knowledge: pretrained language representations are typically trained on big and general-domain corpora. We study the trade-off between employing such large and general-domain corpora versus smaller and domain-specific corpora for training static word embeddings which we evaluate in the analysis of scientific arguments. (C3) Complementarity of knowledge across tasks: many computational argumentation tasks are interrelated but are typically studied in isolation. In two case studies, we show the effectiveness of sharing knowledge across tasks. First, based on a corpus of scientific texts, which we extend with a new annotation layer reflecting fine-grained argumentative structures, we show that coupling the argumentative analysis with other rhetorical analysis tasks leads to performance improvements for the higher-level tasks. In the second case study, we focus on assessing the argumentative quality of texts. To this end, we present a new multi-domain corpus annotated with ratings reflecting different dimensions of argument quality. We then demonstrate the effectiveness of sharing knowledge across the different quality dimensions in multi-task learning setups. (C4) Multilinguality: argumentation arguably exists in all cultures and languages around the globe. To foster inclusive computational argumentation technologies, we dissect the current state-of-the-art in zero-shot cross-lingual transfer. We show big drops in performance when it comes to resource-lean and typologically distant target languages. Based on this finding, we analyze the reasons for these losses and propose to move to inexpensive few-shot target-language transfer, leading to consistent performance improvements in higher-level semantic tasks, e.g., argumentative reasoning. (C5) Ethical considerations: envisioned computational argumentation applications, e.g., systems for self-determined opinion formation, are highly sensitive. We first discuss which ethical aspects should be considered when representing natural language for computational argumentation tasks. Focusing on the issue of unfair stereotypical bias, we then conduct a multi-dimensional analysis of the amount of bias in monolingual and cross-lingual embedding spaces. In the next step, we devise a general framework for implicit and explicit bias evaluation and debiasing. Employing intrinsic bias measures and benchmarks reflecting the semantic quality of the embeddings, we demonstrate the effectiveness of new debiasing methods, which we propose. Finally, we complement this analysis by testing the original as well as the debiased language representations for stereotypically unfair bias in argumentative inferences. We hope that our contributions in language representations for computational argumentation fuel more research on the intersection of the two fields and contribute to fair, efficient, and effective natural language processing technologies

Unsupervised Graph-Based Similarity Learning Using Heterogeneous Features.

Relational data refers to data that contains explicit relations among objects. Nowadays, relational data are universal and have a broad appeal in many different application domains. The problem of estimating similarity between objects is a core requirement for many standard Machine Learning (ML), Natural Language Processing (NLP) and Information Retrieval (IR) problems such as clustering, classiffication, word sense disambiguation, etc. Traditional machine learning approaches represent the data using simple, concise representations such as feature vectors. While this works very well for homogeneous data, i.e, data with a single feature type such as text, it does not exploit the availability of dfferent feature types fully. For example, scientic publications have text, citations, authorship information, venue information. Each of the features can be used for estimating similarity. Representing such objects has been a key issue in efficient mining (Getoor and Taskar, 2007). In this thesis, we propose natural representations for relational data using multiple, connected layers of graphs; one for each feature type. Also, we propose novel algorithms for estimating similarity using multiple heterogeneous features. Also, we present novel algorithms for tasks like topic detection and music recommendation using the estimated similarity measure. We demonstrate superior performance of the proposed algorithms (root mean squared error of 24.81 on the Yahoo! KDD Music recommendation data set and classiffication accuracy of 88% on the ACL Anthology Network data set) over many of the state of the art algorithms, such as Latent Semantic Analysis (LSA), Multiple Kernel Learning (MKL) and spectral clustering and baselines on large, standard data sets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89824/1/mpradeep_1.pd

Feasibility of using citations as document summaries

The purpose of this research is to establish whether it is feasible to use citations as document summaries. People are good at creating and selecting summaries and are generally the standard for evaluating computer generated summaries. Citations can be characterized as concept symbols or short summaries of the document they are citing. Similarity metrics have been used in retrieval and text summarization to determine how alike two documents are. Similarity metrics have never been compared to what human subjects think are similar between two documents. If similarity metrics reflect human judgment, then we can mechanize the selection of citations that act as short summaries of the document they are citing. The research approach was to gather rater data comparing document abstracts to citations about the same document and then to statistically compare those results to several document metrics; frequency count, similarity metric, citation location and type of citation. There were two groups of raters, subject experts and non-experts. Both groups of raters were asked to evaluate seven parameters between abstract and citations: purpose, subject matter, methods, conclusions, findings, implications, readability, andunderstandability. The rater was to identify how strongly the citation represented the content of the abstract, on a five point likert scale. Document metrics were collected for frequency count, cosine, and similarity metric between abstracts and associated citations. In addition, data was collected on the location of the citations and the type of citation. Location was identified and dummy coded for introduction, method, discussion, review of the literature and conclusion. Citations were categorized and dummy coded for whether they refuted, noted, supported, reviewed, or applied information about the cited document. The results show there is a relationship between some similarity metrics and human judgment of similarity.Ph.D., Information Studies -- Drexel University, 200

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc