2 Search Results for "Becker, Maria"

Document
DOI: 10.4230/DagRep.9.7.40
Values in Computing (Dagstuhl Seminar 19291)

Authors: Christoph Becker, Gregor Engels, Andrew Feenberg, Maria Angela Ferrario, and Geraldine Fitzpatrick

Published in: Dagstuhl Reports, Volume 9, Issue 7 (2020)

Abstract
Values are deeply held principles guiding decisions of individuals, groups and organizations. Computing technologies are inevitably affected by values: through their design, values become embodied and enacted. However, some values are easier to quantify and articulate than others; for example, the financial value of a software product is easier to measure than its `fairness'. As a result, less measurable values are often dismissed in decision making processes as lacking evidence. This is particularly problematic since research shows that less measurable values tend to be more strongly associated with sustainable practices than easier to quantify ones; it also indicates that the systems we design are likely to be inadequate for tackling long-term complex societal problems such as environmental change and health-related challenges that so often computing technologies are asked to address. This seminar aims to examine the complex relations between values, computing technologies and society. It does so by bringing together practitioners and researchers from several areas within and beyond computer science, including human computer interaction, software engineering, computer ethics, moral philosophy, philosophy of technology, data science and critical data studies. The outcomes include concrete cases examined through diverse disciplinary perspectives and guidelines for values in computing research, development and education, which are expressed in this report.
Cite as

Christoph Becker, Gregor Engels, Andrew Feenberg, Maria Angela Ferrario, and Geraldine Fitzpatrick. Values in Computing (Dagstuhl Seminar 19291). In Dagstuhl Reports, Volume 9, Issue 7, pp. 40-77, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{becker_et_al:DagRep.9.7.40,
  author =	{Becker, Christoph and Engels, Gregor and Feenberg, Andrew and Ferrario, Maria Angela and Fitzpatrick, Geraldine},
  title =	{{Values in Computing (Dagstuhl Seminar 19291)}},
  pages =	{40--77},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{7},
  editor =	{Becker, Christoph and Engels, Gregor and Feenberg, Andrew and Ferrario, Maria Angela and Fitzpatrick, Geraldine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.9.7.40},
  URN =		{urn:nbn:de:0030-drops-116358},
  doi =		{10.4230/DagRep.9.7.40},
  annote =	{Keywords: computing in society, responsible innovation, sustainability informatics computer ethics, philosophy of technology and moral philosophy}
}
Document
DOI: 10.4230/OASIcs.LDK.2019.8
Exploiting Background Knowledge for Argumentative Relation Classification

Authors: Jonathan Kobbe, Juri Opitz, Maria Becker, Ioana Hulpuş, Heiner Stuckenschmidt, and Anette Frank

Published in: OASIcs, Volume 70, 2nd Conference on Language, Data and Knowledge (LDK 2019)

Abstract
Argumentative relation classification is the task of determining the type of relation (e.g., support or attack) that holds between two argument units. Current state-of-the-art models primarily exploit surface-linguistic features including discourse markers, modals or adverbials to classify argumentative relations. However, a system that performs argument analysis using mainly rhetorical features can be easily fooled by the stylistic presentation of the argument as opposed to its content, in cases where a weak argument is concealed by strong rhetorical means. This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based understanding and representation of arguments. We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. We evaluate our systems on three different datasets and show that the inclusion of background knowledge can improve the classification performance by considerable margins. Thus, our work offers a first step towards effective, knowledge-rich argument analysis.
Cite as

Jonathan Kobbe, Juri Opitz, Maria Becker, Ioana Hulpuş, Heiner Stuckenschmidt, and Anette Frank. Exploiting Background Knowledge for Argumentative Relation Classification. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 8:1-8:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{kobbe_et_al:OASIcs.LDK.2019.8,
  author =	{Kobbe, Jonathan and Opitz, Juri and Becker, Maria and Hulpu\c{s}, Ioana and Stuckenschmidt, Heiner and Frank, Anette},
  title =	{{Exploiting Background Knowledge for Argumentative Relation Classification}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{8:1--8:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Eskevich, Maria and de Melo, Gerard and F\"{a}th, Christian and McCrae, John P. and Buitelaar, Paul and Chiarcos, Christian and Klimek, Bettina and Dojchinovski, Milan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2019.8},
  URN =		{urn:nbn:de:0030-drops-103723},
  doi =		{10.4230/OASIcs.LDK.2019.8},
  annote =	{Keywords: argument structure analysis, background knowledge, argumentative functions, argument classification, commonsense knowledge relations}
}
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