Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Hamborg, Felix"'
Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly.
Externí odkaz:
http://arxiv.org/abs/2410.15148
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descriptions of each entity type: the common label PER might, for example,
Externí odkaz:
http://arxiv.org/abs/2403.14222
Autor:
Hamborg, Felix
This open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/61876
Most NLP tasks are modeled as supervised learning and thus require labeled training data to train effective models. However, manually producing such data at sufficient quality and quantity is known to be costly and time-intensive. Current research ad
Externí odkaz:
http://arxiv.org/abs/2309.09582
Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated d
Externí odkaz:
http://arxiv.org/abs/2112.11914
Autor:
Spinde, Timo, Kreuter, Christina, Gaissmaier, Wolfgang, Hamborg, Felix, Gipp, Bela, Giese, Helge
Media coverage possesses a substantial effect on the public perception of events. The way media frames events can significantly alter the beliefs and perceptions of our society. Nevertheless, nearly all media outlets are known to report news in a bia
Externí odkaz:
http://arxiv.org/abs/2112.07392
Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. To automatically identify biased language, we present an exploratory approach that compares the context of related words. We trai
Externí odkaz:
http://arxiv.org/abs/2112.07384
Publikováno v:
Proceedings of the 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2021) co-located with JCDL 2021, Virtual Event
Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in doma
Externí odkaz:
http://arxiv.org/abs/2112.06724
Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contri
Externí odkaz:
http://arxiv.org/abs/2110.09158
Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable impor
Externí odkaz:
http://arxiv.org/abs/2110.09151