Summary of Textage: a Curated and Diverse Text Dataset For Age Classification, by Shravan Cheekati et al.
TextAge: A Curated and Diverse Text Dataset for Age Classification
by Shravan Cheekati, Mridul Gupta, Vibha Raghu, Pranav Raj
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces TextAge, a comprehensive text dataset that maps sentences to the age and age group of the producer. The dataset is designed to address the lack of diverse datasets in understanding age-related language patterns. It covers a wide range of ages, including spoken and written data from various sources such as CHILDES, Meta, Poki Poems-by-kids, JUSThink, and the TV show “Survivor”. The dataset undergoes extensive cleaning and preprocessing to ensure data quality and consistency. The authors demonstrate the utility of TextAge through two applications: Underage Detection and Generational Classification. For Underage Detection, they train Naive Bayes classifier, fine-tuned RoBERTa, and XLNet models to differentiate between language patterns of minors and young-adults. For Generational Classification, the models classify language patterns into different age groups (kids, teens, twenties, etc.). The results show that the models excel at classifying the “kids” group but struggle with older age groups, likely due to limited data samples and less pronounced linguistic differences. The paper highlights the potential of TextAge for various applications, such as content moderation, targeted advertising, and age-appropriate communication. Future work aims to expand the dataset further and explore advanced modeling techniques to improve performance on older age groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TextAge is a new text dataset that helps us understand how language changes with age. The dataset includes many types of texts from different sources, like children’s poems and TV shows. This allows researchers to study how language patterns change as people get older. The authors show that the dataset can be used for two important tasks: detecting when someone is underage and classifying language patterns by age group. The results are promising, but there’s still room for improvement, especially for older age groups. The authors hope that TextAge will help with tasks like keeping online content suitable for kids and creating targeted ads that are appealing to different age groups. |
Keywords
* Artificial intelligence * Classification * Naive bayes
