Search Results for author: Felix Hamborg

Found 21 papers, 9 papers with code

Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition

no code implementations21 Mar 2024 Jonas Golde, Felix Hamborg, Alan Akbik

In an initial label interpretation learning phase, the model learns to interpret such verbalized descriptions of entity types.

Entity Linking few-shot-ner +4

Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs

1 code implementation18 Sep 2023 Jonas Golde, Patrick Haller, Felix Hamborg, Julian Risch, Alan Akbik

Here, a powerful LLM is prompted with a task description to generate labeled data that can be used to train a downstream NLP model.

Question Answering text-classification +2

Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort

no code implementations15 Dec 2021 Franziska Weeber, Felix Hamborg, Karsten Donnay, Bela Gipp

Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques.

Active Learning Language Modelling +1

Do You Think It's Biased? How To Ask For The Perception Of Media Bias

no code implementations14 Dec 2021 Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, Helge Giese

To name an example: Intending to measure bias in a news article, should we ask, "How biased is the article?"

Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings

no code implementations14 Dec 2021 Timo Spinde, Lada Rudnitckaia, Felix Hamborg, Bela Gipp

The underlying idea is that the context of biased words in different news outlets varies more strongly than the one of non-biased words, since the perception of a word as being biased differs depending on its context.

Word Embeddings

ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

1 code implementation13 Dec 2021 Anastasia Zhukova, Felix Hamborg, Bela Gipp

Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e. g., persons, locations, organizations, and times.

Descriptive named-entity-recognition +2

How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?

no code implementations18 Oct 2021 Felix Hamborg, Timo Spinde, Kim Heinser, Karsten Donnay, Bela Gipp

We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues.

News Recommendation

Newsalyze: Effective Communication of Person-Targeting Biases in News Articles

no code implementations18 Oct 2021 Felix Hamborg, Kim Heinser, Anastasia Zhukova, Karsten Donnay, Bela Gipp

Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles.

Natural Language Understanding

Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes

1 code implementation LREC 2022 Anastasia Zhukova, Felix Hamborg, Bela Gipp

In this paper, we qualitatively and quantitatively compare the annotation schemes of ECB+, a CDCR dataset with identity coreference relations, and NewsWCL50, a CDCR dataset with a mix of loose context-dependent and strict coreference relations.

coreference-resolution Cross Document Coreference Resolution +1

Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons

no code implementations2 Jul 2021 Anastasia Zhukova, Felix Hamborg, Karsten Donnay, Bela Gipp

Specifically, the approach clusters mentions of groups of persons that act as non-named entity actors in the texts, e. g., "migrant families" = "asylum-seekers."

Clustering Dimensionality Reduction +1

Towards Target-dependent Sentiment Classification in News Articles

1 code implementation20 May 2021 Felix Hamborg, Karsten Donnay, Bela Gipp

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media.

Classification Decision Making +3

NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

1 code implementation EACL 2021 Felix Hamborg, Karsten Donnay

Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly.

Decision Making Language Modelling +2

The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity

1 code implementation27 May 2020 Lukas Gebhard, Felix Hamborg

The news dataset is balanced with respect to publication date and outlet popularity.

Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language

no code implementations22 May 2020 Philipp Scharpf, Moritz Schubotz, Abdou Youssef, Felix Hamborg, Norman Meuschke, Bela Gipp

In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content.

Classification Clustering +3

NewsDeps: Visualizing the Origin of Information in News Articles

no code implementations23 Sep 2019 Felix Hamborg, Philipp Meschenmoser, Moritz Schubotz, Bela Gipp

In scientific publications, citations allow readers to assess the authenticity of the presented information and verify it in the original context.

Giveme5W1H: A Universal System for Extracting Main Events from News Articles

2 code implementations6 Sep 2019 Felix Hamborg, Corinna Breitinger, Bela Gipp

Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation.

Clustering Event Extraction

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