Summary Characterizing Latent Perspectives of Media Houses arxiv.org
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One Line
The paper suggests using pre-trained language models like GPT-2 to analyze media perspectives on public figures through a zero-shot approach for generative characterizations.
Slides
Slide Presentation (10 slides)
Key Points
- The paper discusses the characterization of latent perspectives of media houses towards public figures.
- The authors propose a zero-shot approach for non-extractive or generative characterizations using the GPT-2 language model.
- The text mentions the analysis of relational knowledge in pre-trained language models.
- The document discusses the challenges of using large models like GPT-3 for natural language understanding tasks.
- The text highlights the importance of ensuring that every person entity sentence has the full name of the entity.
- The document describes the use of the FT2 corpus for characterizing latent perspectives of media houses.
- Media houses are characterized based on their specific characteristics and actions.
- The study proposes a zero-shot approach to identifying common perceptions and shows good performance in evaluation.
Summaries
24 word summary
This paper explores using pre-trained language models to analyze media houses' perspectives on public figures, proposing a zero-shot approach with GPT-2 for generative characterizations.
37 word summary
This paper discusses the use of pre-trained language models (PLMs) to characterize latent perspectives of media houses towards public figures. The authors propose a zero-shot approach using the GPT-2 language model to generate non-extractive or generative characterizations
372 word summary
This paper discusses the characterization of latent perspectives of media houses towards public figures. The authors propose a zero-shot approach for non-extractive or generative characterizations using the GPT-2 language model. They fine-tune the model with a corpus of
The text excerpt discusses the use of a twice-tuned model to generate text characterizations of entities. The generated text is validated using a bi-directional language model. The text also mentions the analysis of relational knowledge in pre-trained language models, as well
This summary presents the main points of the document "Characterizing Latent Perspectives of Media Houses." The document discusses the use of pre-trained language models (PLMs) for natural language understanding tasks and the challenges of using large models like GPT-3
This excerpt discusses the process of characterizing latent perspectives of media houses. The text mentions the use of prexes and synonymous entities in language models and the importance of ensuring that every person entity sentence has the full name of the entity. The document describes various
The document discusses the characterization of latent perspectives of media houses. It mentions that there are ten different clause types with various combinations of parts. The FT2 corpus is used, which includes sentences with more than 500 sentences. The second ne tuning is done
Media House 3 is characterized as having certain characteristics and performing specific actions. Media House 4 is also described in terms of its characteristics and actions. The text provides examples of novel and meaningful characterizations of various entities within Media House 1, including
Entity S is an active social media user and blogger. Entity T is gaining attention as the new go-to girl. Entity U is dismissive of someone's remarks. Media House 4 (MH4) describes Entity J as a revolutionary and Entity V
There are diverse perspectives about famous personalities and media discourses play a role in shaping these perspectives. Understanding these perspectives is important in the Information Age. This study proposes a zero-shot approach to identifying common perceptions. The evaluation of the approach shows good performance.
The references cited in the document include studies on common-sense knowledge mining from pretrained models [1], clause-based open information extraction [2], improving pre-trained language models as few-shot learners [3], adapting language models to domains and tasks [4],