Search Results for author: Michael Fromm

Found 8 papers, 4 papers with code

Towards a Holistic View on Argument Quality Prediction

no code implementations19 May 2022 Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl

While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.

Argument Mining

Argument Mining Driven Analysis of Peer-Reviews

2 code implementations10 Dec 2020 Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.

Argument Mining

Diversity Aware Relevance Learning for Argument Search

1 code implementation4 Nov 2020 Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman

In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.

Argument Retrieval

Unsupervised Anomaly Detection for X-Ray Images

1 code implementation29 Jan 2020 Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert

Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.

Unsupervised Anomaly Detection

TACAM: Topic And Context Aware Argument Mining

no code implementations26 May 2019 Michael Fromm, Evgeniy Faerman, Thomas Seidl

In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them.

Argument Mining Knowledge Graphs

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