Search Results for author:
Found 21 papers, 9 papers with code
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning
Intermediate task transfer learning can greatly improve model performance.
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
In an initial label interpretation learning phase, the model learns to interpret such verbalized descriptions of entity types.
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Here, a powerful LLM is prompted with a task description to generate labeled data that can be used to train a downstream NLP model.
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort
Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques.
Do You Think It's Biased? How To Ask For The Perception Of Media Bias
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
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.
ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts
Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e. g., persons, locations, organizations, and times.
How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?
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.
Newsalyze: Effective Communication of Person-Targeting Biases in News Articles
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.
XCoref: Cross-document Coreference Resolution in the Wild
Bridging and loose coreference relations trigger associations that may lead to exposing news readers to bias by word choice and labeling.
coreference-resolution
Cross Document Coreference Resolution
Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes
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
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Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons
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."
Towards Target-dependent Sentiment Classification in News Articles
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.
NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
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.
Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling
Media bias can strongly impact the public perception of topics reported in the news.
The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity
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
In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content.
NewsDeps: Visualizing the Origin of Information in News Articles
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
Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation.
Introducing MathQA -- A Math-Aware Question Answering System
This formulae originate from the knowledge-base Wikidata.
