Summary of Arapoembert: a Pretrained Language Model For Arabic Poetry Analysis, by Faisal Qarah
AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis
by Faisal Qarah
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces AraPoemBERT, a pre-trained Arabic language model designed exclusively for analyzing Arabic poetry. The proposed model outperformed five other Arabic language models on various NLP tasks related to Arabic poetry, achieving state-of-the-art results in most downstream tasks. AraPoemBERT demonstrated unprecedented accuracy in poet’s gender classification (99.34%), poetry sub-meter classification (97.79%), and poems’ rhyme classification (97.73%). The model also excelled in sentiment analysis and poetry meter classification, outperforming previous work and comparative models. The study utilized a dataset containing over 2.09 million verses from online sources, each associated with various attributes such as meter, sub-meter, poet, rhyme, and topic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes an Arabic language model just for understanding and analyzing poetry. It’s like having a super smart friend who can read and understand Arabic poetry really well! The new model is much better than other models at doing things like guessing the gender of the poet, identifying the type of meter used, and figuring out whether a poem is happy or sad. This is important because Arabic poetry has special features that make it hard to analyze with regular language models. |
Keywords
» Artificial intelligence » Classification » Language model » Nlp
