Search Results for author: Michael Färber

Found 38 papers, 20 papers with code

GreeDy and CoDy: Counterfactual Explainers for Dynamic Graphs

no code implementations25 Mar 2024 Zhan Qu, Daniel Gomm, Michael Färber

Temporal Graph Neural Networks (TGNNs), crucial for modeling dynamic graphs with time-varying interactions, face a significant challenge in explainability due to their complex model structure.

counterfactual Counterfactual Explanation +1

Embedded Named Entity Recognition using Probing Classifiers

no code implementations18 Mar 2024 Nicholas Popovič, Michael Färber

Extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation.

Fact Checking Language Modelling +5

Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models

no code implementations28 Feb 2024 Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze

Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities.

Part-Of-Speech Tagging Sentence

Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers

no code implementations18 Feb 2024 Shuzhou Yuan, Ercong Nie, Bolei Ma, Michael Färber

Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks.

text-classification Text Classification

GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network

no code implementations18 Feb 2024 Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, Hinrich Schütze

Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are applied to them.

In-Context Learning text-classification +1

ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

1 code implementation29 Jan 2024 Bolei Ma, Ercong Nie, Shuzhou Yuan, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze

However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.

Benchmarking In-Context Learning +8

Analyzing the Impact of Companies on AI Research Based on Publications

1 code implementation31 Oct 2023 Michael Färber, Lazaros Tampakis

Artificial Intelligence (AI) is one of the most momentous technologies of our time.

Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph

1 code implementation31 Oct 2023 Michael Färber, David Lamprecht

In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400, 000 machine learning publications.

Knowledge Graph Embeddings

A Full-fledged Commit Message Quality Checker Based on Machine Learning

1 code implementation9 Sep 2023 David Faragó, Michael Färber, Christian Petrov

By considering all rules from the most popular CM quality guideline, creating datasets for those rules, and training and evaluating state-of-the-art machine learning models to check those rules, we can answer the research question with: sufficiently well for practice, with the lowest F$_1$ score of 82. 9\%, for the most challenging task.

SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

no code implementations7 Aug 2023 Michael Färber, David Lamprecht, Johan Krause, Linn Aung, Peter Haase

We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts.

Recommendation Systems

Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election

no code implementations7 Aug 2023 Michael Färber, Jannik Schwade, Adam Jatowt

Determining and measuring diversity in news articles is important for a number of reasons, including preventing filter bubbles and fueling public discourse, especially before elections.

Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

no code implementations7 Aug 2023 Michael Färber, Nicholas Popovic

In this paper, we propose Vocab-Expander at https://vocab-expander. com, an online tool that enables end-users (e. g., technology scouts) to create and expand a vocabulary of their domain of interest.

Common Sense Reasoning Information Retrieval +3

Evaluating Generative Models for Graph-to-Text Generation

1 code implementation27 Jul 2023 Shuzhou Yuan, Michael Färber

Large language models (LLMs) have been widely employed for graph-to-text generation tasks.

Descriptive Text Generation

unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network

1 code implementation27 Mar 2023 Tarek Saier, Johan Krause, Michael Färber

Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications.

Citation Recommendation

CoCon: A Data Set on Combined Contextualized Research Artifact Use

1 code implementation27 Mar 2023 Tarek Saier, Youxiang Dong, Michael Färber

To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text.

Link Prediction

Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation

no code implementations18 Jan 2023 Michael Färber, Melissa Coutinho, Shuzhou Yuan

With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, academic recommender systems have become increasingly important for millions of researchers and science enthusiasts.

Recommendation Systems

Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis

no code implementations13 Nov 2022 Tanja Aue, Adam Jatowt, Michael Färber

Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years.

Are Investors Biased Against Women? Analyzing How Gender Affects Startup Funding in Europe

no code implementations1 Dec 2021 Michael Färber, Alexander Klein

For startup founders, it is therefore crucial to know whether investors have a bias against women as startup founders and in which way startups face disadvantages due to gender bias.

Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text

1 code implementation30 Nov 2021 Michael Färber, Anna Steyer

We suggest (1) to combine the keyword search with precomputed topic clusters for argument-query matching, (2) to apply a novel approach based on sentence-level sequence-labeling for argument identification, and (3) to present aggregated arguments to users based on topic-aware argument clustering.

Clustering Sentence

Explaining Convolutional Neural Networks by Tagging Filters

no code implementations20 Sep 2021 Anna Nguyen, Daniel Hagenmayer, Tobias Weller, Michael Färber

Finally, we show that the tags are helpful in analyzing classification errors caused by noisy input images and that the tags can be further processed by machines.

Classification Image Classification

Safe, Fast, Concurrent Proof Checking for the lambda-Pi Calculus Modulo Rewriting

no code implementations17 Feb 2021 Michael Färber

Several proof assistants, such as Isabelle or Coq, can concurrently check multiple proofs.

Logic in Computer Science

Right for the Right Reason: Making Image Classification Robust

no code implementations23 Jul 2020 Anna Nguyen, Adrian Oberföll, Michael Färber

To this end, we propose a new explanation quality metric to measure object aligned explanation in image classification which we refer to as theObAlExmetric.

Classification General Classification +4

Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation

1 code implementation ECIR 2020 Tarek Saier, Michael Färber

New research is being published at a rate, at which it is infeasible for many scholars to read and assess everything possibly relevant to their work.

Citation Recommendation

unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata

1 code implementation Scientometrics 2020 Tarek Saier, Michael Färber

The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

Citation Recommendation Document Summarization +3

Citation Recommendation: Approaches and Datasets

1 code implementation17 Feb 2020 Michael Färber, Adam Jatowt

In recent years, several approaches and evaluation data sets have been presented.

Citation Recommendation

HybridCite: A Hybrid Model for Context-Aware Citation Recommendation

3 code implementations15 Feb 2020 Michael Färber, Ashwath Sampath

The process of recommending citations for citation contexts is called local citation recommendation and is the focus of this paper.

Citation Recommendation Information Retrieval +2

Making Neural Networks FAIR

1 code implementation26 Jul 2019 Anna Nguyen, Tobias Weller, Michael Färber, York Sure-Vetter

In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles.

Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies

1 code implementation19 Jul 2019 Michael Färber

Crunchbase is an online platform collecting information about startups and technology companies, including attributes and relations of companies, people, and investments.

Which Knowledge Graph Is Best for Me?

no code implementations28 Sep 2018 Michael Färber, Achim Rettinger

Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting.

Knowledge Graphs

Monte Carlo Tableau Proof Search

no code implementations18 Nov 2016 Michael Färber, Cezary Kaliszyk, Josef Urban

We study Monte Carlo Tree Search to guide proof search in tableau calculi.

Automated Theorem Proving

Internal Guidance for Satallax

no code implementations30 May 2016 Michael Färber, Chad Brown

We evaluated our method on a simply-typed higher-order logic version of the Flyspeck project, where it solves 26% more problems than Satallax without internal guidance.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.