Search Results for author: Lukas Gienapp

Found 8 papers, 4 papers with code

SMAuC -- The Scientific Multi-Authorship Corpus

no code implementations4 Nov 2022 Janek Bevendorff, Philipp Sauer, Lukas Gienapp, Wolfgang Kircheis, Erik Körner, Benno Stein, Martin Potthast

The rapidly growing volume of scientific publications offers an interesting challenge for research on methods for analyzing the authorship of documents with one or more authors.

Sparse Pairwise Re-ranking with Pre-trained Transformers

1 code implementation10 Jul 2022 Lukas Gienapp, Maik Fröbe, Matthias Hagen, Martin Potthast

Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences.

Passage Ranking Re-Ranking +1

Tracking Discourse Influence in Darknet Forums

1 code implementation4 Feb 2022 Christopher Akiki, Lukas Gienapp, Martin Potthast

This technical report documents our efforts in addressing the tasks set forth by the 2021 AMoC (Advanced Modelling of Cyber Criminal Careers) Hackathon.

STEREO: Scientific Text Reuse in Open Access Publications

1 code implementation22 Dec 2021 Lukas Gienapp, Wolfgang Kircheis, Bjarne Sievers, Benno Stein, Martin Potthast

We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications.

The Impact of Main Content Extraction on Near-Duplicate Detection

no code implementations21 Nov 2021 Maik Fröbe, Matthias Hagen, Janek Bevendorff, Michael Völske, Benno Stein, Christopher Schröder, Robby Wagner, Lukas Gienapp, Martin Potthast

Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages.

Information Retrieval Retrieval

Efficient Pairwise Annotation of Argument Quality

no code implementations ACL 2020 Lukas Gienapp, Benno Stein, Matthias Hagen, Martin Potthast

We present an efficient annotation framework for argument quality, a feature difficult to be measured reliably as per previous work.

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