1 code implementation • SemEval (NAACL) 2022 • Nicholas Popovic, Walter Laurito, Michael Färber
In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models.
1 code implementation • NAACL (sdp) 2021 • Johan Krause, Igor Shapiro, Tarek Saier, Michael Färber
Applications based on scholarly data are of ever increasing importance.
no code implementations • 10 Apr 2024 • Shuzhou Yuan, Michael Färber
Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks.
no code implementations • 25 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.
no code implementations • 18 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.
no code implementations • 28 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.
no code implementations • 18 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.
no code implementations • 18 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.
1 code implementation • 29 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.
1 code implementation • 17 Dec 2023 • Tarek Saier, Mayumi Ohta, Takuto Asakura, Michael Färber
We create a labeled data set covering publications from a variety of computer science disciplines.
1 code implementation • 31 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.
1 code implementation • 31 Oct 2023 • Michael Färber, Lazaros Tampakis
Artificial Intelligence (AI) is one of the most momentous technologies of our time.
1 code implementation • 9 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.
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 7 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.
1 code implementation • 27 Jul 2023 • Shuzhou Yuan, Michael Färber
Large language models (LLMs) have been widely employed for graph-to-text generation tasks.
1 code implementation • 27 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.
1 code implementation • 27 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.
no code implementations • 18 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.
no code implementations • 13 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.
1 code implementation • NAACL 2022 • Nicholas Popovic, Michael Färber
We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark.
1 code implementation • 10 Mar 2022 • Nicholas Popovic, Walter Laurito, Michael Färber
In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models.
no code implementations • 1 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.
1 code implementation • 30 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.
1 code implementation • 7 Nov 2021 • Tarek Saier, Michael Färber, Tornike Tsereteli
Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse.
Citation Intent Classification Cross-Lingual Entity Linking +1
no code implementations • 20 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.
no code implementations • 17 Feb 2021 • Michael Färber
Several proof assistants, such as Isabelle or Coq, can concurrently check multiple proofs.
Logic in Computer Science
no code implementations • 23 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.
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.
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.
1 code implementation • 17 Feb 2020 • Michael Färber, Adam Jatowt
In recent years, several approaches and evaluation data sets have been presented.
3 code implementations • 15 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.
1 code implementation • 26 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.
1 code implementation • 19 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.
no code implementations • 28 Sep 2018 • Michael Färber, Achim Rettinger
Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting.
no code implementations • 18 Nov 2016 • Michael Färber, Cezary Kaliszyk, Josef Urban
We study Monte Carlo Tree Search to guide proof search in tableau calculi.
no code implementations • 30 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.