Search Results for author: Michael Völske

Found 4 papers, 1 papers with code

Language Models as Context-sensitive Word Search Engines

1 code implementation In2Writing (ACL) 2022 Matti Wiegmann, Michael Völske, Benno Stein, Martin Potthast

Context-sensitive word search engines are writing assistants that support word choice, phrasing, and idiomatic language use by indexing large-scale n-gram collections and implementing a wildcard search.

Language Modelling

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

Towards Axiomatic Explanations for Neural Ranking Models

no code implementations15 Jun 2021 Michael Völske, Alexander Bondarenko, Maik Fröbe, Matthias Hagen, Benno Stein, Jaspreet Singh, Avishek Anand

We investigate whether one can explain the behavior of neural ranking models in terms of their congruence with well understood principles of document ranking by using established theories from axiomatic IR.

Document Ranking Information Retrieval +1

Heuristic Feature Selection for Clickbait Detection

no code implementations4 Feb 2018 Matti Wiegmann, Michael Völske, Benno Stein, Matthias Hagen, Martin Potthast

We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017.

Clickbait Detection Feature Engineering +1

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