Search Results for author: Martin Potthast

Found 28 papers, 9 papers with code

Small-text: Active Learning for Text Classification in Python

1 code implementation21 Jul 2021 Christopher Schröder, Lydia Müller, Andreas Niekler, Martin Potthast

We present small-text, a simple modular active learning library, which offers pool-based active learning for text classification in Python.

Active Learning Text Classification

Uncertainty-based Query Strategies for Active Learning with Transformers

no code implementations12 Jul 2021 Christopher Schröder, Andreas Niekler, Martin Potthast

Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings.

Active Learning Text Classification

Generating Informative Conclusions for Argumentative Texts

1 code implementation2 Jun 2021 Shahbaz Syed, Khalid Al-Khatib, Milad Alshomary, Henning Wachsmuth, Martin Potthast

Third, insights are provided into the suitability of our corpus for the task, the differences between the two generation paradigms, the trade-off between informativeness and conciseness, and the impact of encoding argumentative knowledge.

Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis

no code implementations ACL 2020 Janek Bevendorff, Khalid Al Khatib, Martin Potthast, Benno Stein

This paper introduces the Webis Gmane Email Corpus 2019, the largest publicly available and fully preprocessed email corpus to date.

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.

Target Inference in Argument Conclusion Generation

no code implementations ACL 2020 Milad Alshomary, Shahbaz Syed, Martin Potthast, Henning Wachsmuth

In particular, we argue here that a decisive step is to infer a conclusion{'}s target, and we hypothesize that this target is related to the premises{'} targets.

Abstractive Snippet Generation

1 code implementation25 Feb 2020 Wei-Fan Chen, Shahbaz Syed, Benno Stein, Matthias Hagen, Martin Potthast

An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page.

Text Summarization

Common Conversational Community Prototype: Scholarly Conversational Assistant

no code implementations19 Jan 2020 Krisztian Balog, Lucie Flekova, Matthias Hagen, Rosie Jones, Martin Potthast, Filip Radlinski, Mark Sanderson, Svitlana Vakulenko, Hamed Zamani

This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions.

Conversational Search Platform

Towards Summarization for Social Media - Results of the TL;DR Challenge

no code implementations WS 2019 Shahbaz Syed, Michael V{\"o}lske, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze, Martin Potthast

In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.

Bias Analysis and Mitigation in the Evaluation of Authorship Verification

1 code implementation ACL 2019 Janek Bevendorff, Matthias Hagen, Benno Stein, Martin Potthast

The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics.

Authorship Verification

Celebrity Profiling

1 code implementation ACL 2019 Matti Wiegmann, Benno Stein, Martin Potthast

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers.

Gender Prediction Occupation prediction +1

Heuristic Authorship Obfuscation

1 code implementation ACL 2019 Janek Bevendorff, Martin Potthast, Matthias Hagen, Benno Stein

Authorship verification is the task of determining whether two texts were written by the same author.

Authorship Verification

Task Proposal: The TL;DR Challenge

no code implementations WS 2018 Shahbaz Syed, Michael V{\"o}lske, Martin Potthast, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far.

Abstractive Text Summarization Information Retrieval +1

CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

no code implementations CONLL 2018 Daniel Zeman, Jan Haji{\v{c}}, Martin Popel, Martin Potthast, Milan Straka, Filip Ginter, Joakim Nivre, Slav Petrov

Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.

Dependency Parsing Morphological Analysis +2

Crowdsourcing a Large Corpus of Clickbait on Twitter

no code implementations COLING 2018 Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Garces Fern, Erika Patricia ez, Matthias Hagen, Benno Stein

To address the urging task of clickbait detection, we constructed a new corpus of 38, 517 annotated Twitter tweets, the Webis Clickbait Corpus 2017.

Clickbait Detection

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 +2

TL;DR: Mining Reddit to Learn Automatic Summarization

no code implementations WS 2017 Michael V{\"o}lske, Martin Potthast, Shahbaz Syed, Benno Stein

Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data.

Abstractive Text Summarization Document Summarization

A Stylometric Inquiry into Hyperpartisan and Fake News

1 code implementation ACL 2018 Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, Benno Stein

The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing.

Authorship Verification Fake News Detection +1

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