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Yamen Ajjour
Researcher at Martin Luther University of Halle-Wittenberg
Publications - 15
Citations - 431
Yamen Ajjour is an academic researcher from Martin Luther University of Halle-Wittenberg. The author has contributed to research in topics: Computer science & Argument. The author has an hindex of 7, co-authored 11 publications receiving 305 citations. Previous affiliations of Yamen Ajjour include Bauhaus University, Weimar.
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Proceedings ArticleDOI
Building an Argument Search Engine for the Web
Henning Wachsmuth,Martin Potthast,Khalid Al Khatib,Yamen Ajjour,Jana Puschmann,Jiani Qu,Jonas Dorsch,Viorel Morari,Janek Bevendorff,Benno Stein +9 more
TL;DR: An argument search framework for studying how people query for arguments, how to mine arguments from the web, or how to rank them is developed and a prototype search engine is built that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources.
Proceedings ArticleDOI
Unit Segmentation of Argumentative Texts
TL;DR: This paper studies the major parameters of unit segmentation systematically, and explores the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text.
Book ChapterDOI
Data Acquisition for Argument Search: The args.me Corpus
TL;DR: This paper presents the new corpus of the argument search engine args.me, which follows the former paradigm, and freely provides the corpus to the community, which is one of the largest argument resources available so far.
Proceedings ArticleDOI
Modeling Frames in Argumentation
TL;DR: This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames, and presents a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering.
Proceedings ArticleDOI
"PageRank" for Argument Relevance
TL;DR: A radical model to assess relevance objectively at web scale is proposed: the relevance of an argument’s conclusion is decided by what other arguments reuse it as a premise, and an argument graph is built for this model, adapting key ideas of PageRank.