Search Results for author: Evgeniy Faerman

Found 15 papers, 11 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

Prediction of soft proton intensities in the near-Earth space using machine learning

1 code implementation11 May 2021 Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher, Marcus Münzer, Florian Peste, Ziyang Zhou, Max Berrendorf, Evgeniy Faerman, Fabio Gastaldello, Simona Ghizzardi, Philippe Escoubet, Stein Haaland, Artem Smirnov, Nithin Sivadas, Robert C. Allen, Andrea Tiengo, Raluca Ilie

The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.

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

A Critical Assessment of State-of-the-Art in Entity Alignment

1 code implementation30 Oct 2020 Max Berrendorf, Ludwig Wacker, Evgeniy Faerman

Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable.

Entity Alignment Hyperparameter Optimization +1

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

1 code implementation23 Oct 2020 Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl

Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.

Retinal OCT Disease Classification Semi-Supervised Image Classification +1

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering

1 code implementation27 Sep 2020 Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl

Consequently, our model benefits from a constant number of parameters and a constant-size memory footprint, allowing it to scale to considerably larger datasets.

Metric Learning

On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

1 code implementation17 Feb 2020 Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp

In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.

Entity Alignment Informativeness +2

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

Active Learning for Entity Alignment

1 code implementation24 Jan 2020 Max Berrendorf, Evgeniy Faerman, Volker Tresp

In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.

Active Learning Entity Alignment

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

1 code implementation19 Nov 2019 Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl

In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.

Entity Alignment Knowledge Graphs

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

Semi-Supervised Learning on Graphs Based on Local Label Distributions

no code implementations15 Feb 2018 Evgeniy Faerman, Felix Borutta, Julian Busch, Matthias Schubert

Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node.

General Classification Node Classification

LASAGNE: Locality And Structure Aware Graph Node Embedding

no code implementations17 Oct 2017 Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney

For larger graphs with flat NCPs that are strongly expander-like, existing methods lead to random walks that expand rapidly, touching many dissimilar nodes, thereby leading to lower-quality vector representations that are less useful for downstream tasks.

Link Prediction Multi-Label Classification

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