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researcher

Michael Färber

9 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author1
  • last author2

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.DL2
  • cs.AI1
  • cs.IR1
  • cs.SE1
ORCID 0000-0001-5458-8645
same name
  • Michael Färber — 8 papers
  • Michael Färber — 5 papers
  • Michael Färber — 2 papers, h 6
  • Michael Färber — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedAnalyzing the Impact of Companies on AI Research Based on Publications

3 citations · 13 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023

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

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 elect…

cs.CL2023

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

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 d…

cs.CL2023★ 3 cited

Evaluating Generative Models for Graph-to-Text Generation

Shuzhou Yuan, Michael Färber

Large language models (LLMs) have been widely employed for graph-to-text generation tasks. However, the process of finetuning LLMs requires significant training resources and annot…

cs.CL2022★ 1 cited

Few-Shot Document-Level Relation Extraction

Nicholas Popovic, Michael Färber

We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora,…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.