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Geberit Quotes

After running pilots with several annotation providers, Label Your Data delivered the strongest results by a clear margin, standing out on turnaround time, annotation quality, and the responsiveness of their feedback loops.

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Geberit
Maxime Debarbat

Maxime Debarbat

Senior ML Engineer (GenAI)

Trusted by ML Professionals

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Academia

Linguistic Annotation for Technological University Dublin

Location:
Ireland Ireland
Services:
NLP Annotation
Technological University Dublin

Overview

Kyle Hamilton from Technological University Dublin collaborated with Label Your Data for her research on rhetorical devices of propaganda used in news articles.

357 sentences
22 categories
3 annotators
Client

Client

Kyle Hamilton is a PhD Researcher at TU Dublin who focuses on applying neuro-symbolic AI to detect propaganda in news feeds.

Challenges

Challenges

Kyle needed a high-quality text dataset containing classified propaganda-related sentences to compare humans and ChatGPT in detecting propaganda in news.

Solutions

Solutions

Label Your Data provided skilled annotators with linguistic background to classify and label 357 sentences using the Client’s platform.

Results

Results

Despite inconsistencies in human annotations, they prove more reliable in propaganda analysis due to ChatGPT’s lack of real-world knowledge.

Client

Kyle Hamilton is a PhD Researcher at Technological University Dublin. Her research work is centered around neuro-symbolic AI for detecting propaganda in news articles.

Holding a Master’s in Information and Data Science and a Bachelor of Fine Art, Kyle brings a unique interdisciplinary approach to her work.

Rhetoric Annotation Tool

Rhetoric Annotation Tool

Challenges

Most efforts to automate the detection of propaganda and misinformation are mainly focused on using natural language processing (NLP).

Following the same idea, Kyle aimed to create a tool that helps identify propaganda in news. But first, she wanted to compare ChatGPT and human linguists in performing the same task.

Kyle Hamilton needed help with:

1

Exploring the possibilities of using ChatGPT for automated propaganda detection.

2

Hiring an expert annotation team with domain experts to compare human annotations to ChatGPT’s responses.

3

Getting high-quality annotated text dataset for the research.

4

Dealing with the subjective nature of the sentence classification task.

Solution

Ratio of partial agreement among all three annotators for each feature

Chat GPT Agreement among itself when prompted 3 times

Verb choices

227
202

Tropes

253
220

Tense

308
297

Subject choices

312
306

Series

40
34

Sentence architecture

292
281

Prosody and punctuation

4
-

Predication

308
311

Phrases built on verbs

147
147

Phrases built on nouns

70
60

Parallelism

106
43

New words and changing uses

252
250

Mood

316
308

Modifying phrases

307
310

Modifying clauses

226
212

Lexical and semantic fields

316
320

Language varieties

316
315

Language of origin

316
299

Figures of word choice

213
169

Figures of argument

125
106

Emphasis

316
299

Aspect

309
294

Label Your Data helped with:

1

Hiring 3 dedicated data annotators with linguistic background for classification of 357 sentences.

2

Working in a flexible mode to seamlessly integrate into Kyle's annotation platform.

3

Conducting a cross-reference QA to address the project’s subjectivity.

4

Delivering high-quality annotated text corpus.

Results

This initial phase aimed at supporting Kyle Hamilton’s research and securing the grant. While findings are initial, Label Your Data expects a larger dataset to annotate.

Though human experts’ annotations vary, their real-world knowledge surpasses AI models like ChatGPT, which solely relies on internet-derived data.

1

Analyzed the discrepancies in agreement between annotators and ChatGPT.

2

Identified the most challenging areas to achieve consensus:

Checked Series
Checked Prosody and punctuation
Checked Phrases built on verbs
Checked Parallelism
Checked Figures of word choice
Checked Figures of argument
3

Despite inconsistencies, defined human annotators as more reliable for propaganda analysis.

4

Delivering high-quality annotated text corpus.

Request a pilot

Tell us more about your project and data

Email is not valid.

Email is not valid

Company name is not valid

Phone is not valid

Some error text

Referrer domain is wrong

Thank you for contacting us!

Thank you for contacting us!

We'll get back to you shortly

Geberit Quotes

After running pilots with several annotation providers, Label Your Data delivered the strongest results by a clear margin, standing out on turnaround time, annotation quality, and the responsiveness of their feedback loops.

Quotes
Geberit
Maxime Debarbat

Maxime Debarbat

Senior ML Engineer (GenAI)

Trusted by ML Professionals

Ouster
Searidge Technologies
Zendar
Advanced Farm
ABB
Toptal
UiPath
Respeecher
Yale
Thorvald

Why AI Teams Choose Label Your Data

Data Annotation for Complex Environments

Data Annotation for Complex Environments

Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.

Structured Quality from Pilot to Production

Structured Quality from Pilot to Production

Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.

Flexible and Scalable Operations

Flexible and Scalable Operations

Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.

An Integrated Delivery Partner

An Integrated Delivery Partner

Align on goals, workflows, and expectations with a team that integrates into your process from day one.

Projects Led by Annotation Experts

Projects Led by Annotation Experts

Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.