---
title: "POS Tagging for Arabic Tweets — Albogamy 2015"
description: "The paper asks how well Arabic part-of-speech taggers built for Modern Standard Arabic… 0% accuracy. 23 references, cited by 6 papers here."
authors: ["Fahad Albogamy", "Allan Ramsay"]
year: 2015
venue: "Proceedings of the International Conference Recent Advances in Natural Language Processing"
source: "https://aclanthology.org/R15-1001/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/albogamy-2015-pos-tagging-arabic-tweets/"
---
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# POS Tagging for Arabic Tweets

**Authors:** [Fahad Albogamy](https://md.research.elamur.ai/free/en/authors/fahad-albogamy/) and [Allan Ramsay](https://md.research.elamur.ai/free/en/authors/allan-ramsay/) · **Year:** 2015 · **Venue:** [Proceedings of the International Conference Recent Advances in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/ranlp/) ([RANLP 2015](https://md.research.elamur.ai/free/en/venues/ranlp/2015/)) · **Publisher:** [INCOMA Ltd.](https://md.research.elamur.ai/free/en/publishers/incoma-ltd/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/R15-1001/) · [PDF](https://aclanthology.org/R15-1001.pdf) · [OpenAlex](https://openalex.org/W2399571110)

**Citations:** 20 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/albogamy-2015-pos-tagging-arabic-tweets/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks how well Arabic part-of-speech taggers built for Modern Standard Arabic (MSA) cope with Twitter, where texts are short, informal, often dialectal, and mixed with romanised Arabic, foreign words, hashtags and emoji. The authors used the Twitter Stream API with geographic coordinates for the Arabian Peninsula and Egypt to collect tweets. They sampled 390 tweets (5454 words) and built a gold standard by hand-correcting tagger output. They added new tags for Twitter-specific elements. Three off-the-shelf taggers trained on newswire were tested: AMIRA, MADA and the Stanford Log-linear tagger. Their accuracy fell from roughly 96–97% to 49–66%. A detailed error analysis showed that most failures come from unknown tokens, which the authors grouped into many categories. Guided by these errors, they wrapped the taggers in preprocessing and postprocessing steps, using normalisation plus external knowledge such as gazetteers and an English tagger. This raised accuracy by 12–16 points, with the best result at 79% (MADA). The work is an early baseline and diagnosis for tagging Arabic social media without building a new tagger from scratch.

## Key points
- Corpus: 390 tweets (5454 words) from the Arabian Peninsula and Egypt. Tweets were not restricted to the Arabic language, so romanised or mixed tweets were kept, but fully English tweets were removed. New tags were added: REP, MEN, HASH, LINK, USERN, RET.
- Baseline accuracy on newswire vs. tweets: AMIRA 96.0% vs. 60.2%, MADA 97.0% vs. 65.8%, Stanford 96.5% vs. 49.0%.
- Arabic-script words make up 73.5% (AMIRA), 68.1% (MADA) and 79.2% (Stanford) of the errors. Twitter-specific tokens account for 15–23% of errors, and every tagger gets 0% accuracy on them.
- Even clean MSA words inside tweets are tagged worse than in newswire: 71.8% (AMIRA), 79.3% (MADA) and 55.0% (Stanford). The authors blame noisy context, dropped function words and domain shift from the Arabic Treebank.
- After processing, accuracy rose to 72.6% (AMIRA), 79.0% (MADA) and 65.2% (Stanford). Planned next step: agreement-based bootstrapping, following Clark et al. 2003, on a unified tagset, then retraining Stanford.

## Interesting ideas
- Break the errors into a fine taxonomy and report both each category's share of all errors and the tagger's accuracy within it, so it is clear which fixes are worth the effort.
- Keep strong existing taggers and adapt them through wrapper steps (normalise input, split hashtags, mark them, retag afterwards) instead of training a domain-specific model.
- Give platform-specific tokens (mentions, links, retweet markers) their own tags, so they never reach the tagger and do not corrupt the context around real words.
- Send tokens in another language or script to a tagger built for that language, and use name gazetteers to fix unknown proper nouns.
- Create training data by keeping only the tokens on which several different taggers agree, after mapping their tagsets to one shared coarse tagset.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Our evaluation reports could list per-phenomenon error share and accuracy, which would rank the self-induced repair rules by how much damage each category causes.
- **I2** (tokens): Messy input to crate en could first go through a reversible normalisation layer whose every edit is logged, keeping the core tagger unchanged and the result explainable.
- **I3** (tokens): The tokenizer could detect URLs, handles and similar items early and assign them fixed categories, which would protect the tags of neighbouring words.
- **I4** (tokens): Unknown capitalised words could be checked against Wikidata-derived name lists before the tagger guesses, giving a traceable reason for each PROPN decision.
- **I5** (learn): This supports our multi-witness silver data, and mapping each witness parser's labels to one UD layer first is a step worth formalising.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Manchester](https://md.research.elamur.ai/free/en/universities/university-of-manchester/) — University, Manchester, United Kingdom · 93 works · 45,138 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works · [🔤 Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) · 126 works

## Cite

Cite as: Fahad Albogamy and Allan Ramsay. 2015. POS Tagging for Arabic Tweets. Proceedings of the International Conference Recent Advances in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{albogamy2015pos,
  title = {{POS Tagging for Arabic Tweets}},
  author = {Fahad Albogamy and Allan Ramsay},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing},
  year = {2015},
  url = {https://aclanthology.org/R15-1001/}
}
```

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## References

23 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Muhammad Abdul-Mageed, Sandra Kübler, and Mona Diab. 2012. [SAMAR: A system for subjectivity and sentiment analysis of Arabic social media](https://md.research.elamur.ai/free/en/papers/mageed-2012-samar-system-subjectivity-sentiment-analysis/). In Proceedings of WASSA.
2. Rania Al-Sabbagh and Roxana Girju. 2012. A supervised POS tagger for written Arabic social networking corpora. In Proceedings of KONVENS. [find](https://scholar.google.com/scholar?q=Rania+Al%2DSabbagh+and+Roxana+Girju%2E+2012%2E+A+supervised+POS+tagger+for+written+Arabic+social+networking+corpora%2E+In+Proceedings+of+KONVENS%2E)
3. Fahad Albogamy and Allan Ramsay. 2015. [Towards POS tagging for Arabic tweets](https://md.research.elamur.ai/free/en/papers/albogamy-2015-pos-tagging-arabic-tweets-w15-4325/). In Proceedings of ACL Workshop on Noisy User-generated Text.
4. Luciano Barbosa and Junlan Feng. 2010. [Robust sentiment detection on twitter from biased and noisy data](https://md.research.elamur.ai/free/en/papers/barbosa-2010-robust-sentiment-detection-twitter-biased/). In Proceedings of ACL.
5. Eric Brill. 1995. [Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging](https://md.research.elamur.ai/free/en/papers/brill-1995-transformation-based-error-driven-learning/). Computational Linguistics.
6. Stephen Clark, James R. Curran, and Miles Osborne. 2003. Bootstrapping POS taggers using unlabelled data. In Proceedings of NAACL. ACL. [find](https://scholar.google.com/scholar?q=Stephen+Clark%2C+James+R%2E+Curran%2C+and+Miles+Osborne%2E+2003%2E+Bootstrapping+POS+taggers+using+unlabelled+data%2E+In+Proceedings+of+NAACL%2E+ACL%2E)
7. Silviu Cucerzan and David Yarowsky. 2002. Bootstrapping a multilingual part-of-speech tagger in one person-day. In Proceedings of NLL. ACL. [find](https://scholar.google.com/scholar?q=Silviu+Cucerzan+and+David+Yarowsky%2E+2002%2E+Bootstrapping+a+multilingual+part%2Dof%2Dspeech+tagger+in+one+person%2Dday%2E+In+Proceedings+of+NLL%2E+ACL%2E)
8. Leon Derczynski, Alan Ritter, Sam Clark, and Kalina Bontcheva. 2013. [Twitter part-of-speech tagging for all: Overcoming sparse and noisy data](https://md.research.elamur.ai/free/en/papers/derczynski-2013-twitter-part-speech-tagging-all/). In Proceedings of RANLP.
9. Mona Diab. 2009. Second generation AMIRA tools for Arabic processing: Fast and robust tokenization, POS tagging, and base phrase chunking. In 2nd International Conference on Arabic Language Resources and Tools. [find](https://scholar.google.com/scholar?q=Mona+Diab%2E+2009%2E+Second+generation+AMIRA+tools+for+Arabic+processing%3A+Fast+and+robust+tokenization%2C+POS+tagging%2C+and+base+phrase+chunking%2E+In+2nd+International+Conference+on+Arabic+Language+Resources+)
10. Nawal El-Fishawy, Alaa Hamouda, Gamal M. Attiya, and Mohammed Atef. 2014. Arabic summarization in twitter social network. Ain Shams Engineering Journal. [↗](https://doi.org/10.1016/j.asej.2013.11.002)
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12. Jennifer Foster, Özlem Çetinoglu, Joachim Wagner, Joseph Le Roux, Stephen Hogan, Joakim Nivre, Deirdre Hogan, Josef Van Genabith, et al. 2011. # hardtoparse: POS tagging and parsing the twitterverse. In Proceedings of AAAI. [find](https://scholar.google.com/scholar?q=Jennifer+Foster%2C+O%CC%88zlem+C%CC%A7etinoglu%2C+Joachim+Wagner%2C+Joseph+Le+Roux%2C+Stephen+Hogan%2C+Joakim+Nivre%2C+Deirdre+Hogan%2C+Josef+Van+Genabith%2C+et+al%2E+2011%2E+%23+hardtoparse%3A+POS+tagging+and+parsing+the+twitterverse)
13. Phani Gadde, L. V. Subramaniam, and Tanveer A. Faruquie. 2011. Adapting a WSJ trained part-of-speech tagger to noisy text: Preliminary results. In Proceedings of MOCR. [find](https://scholar.google.com/scholar?q=Phani+Gadde%2C+L%2E+V%2E+Subramaniam%2C+and+Tanveer+A%2E+Faruquie%2E+2011%2E+Adapting+a+WSJ+trained+part%2Dof%2Dspeech+tagger+to+noisy+text%3A+Preliminary+results%2E+In+Proceedings+of+MOCR%2E)
14. Kevin Gimpel, Nathan Schneider, Brendan O’Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith. 2011. [Part-of-speech tagging for Twitter: Annotation, features, and experiments](https://md.research.elamur.ai/free/en/papers/gimpel-2011-part-speech-tagging-twitter-annotation/). In Proceedings of ACL: HLT. [↗](https://doi.org/10.21236/ada547371)
15. Nizar Habash, Owen Rambow, and Ryan Roth. 2009. Mada+ tokan: A toolkit for Arabic tokenization, diacritization, morphological disambiguation, POS tagging, stemming and lemmatization. [find](https://scholar.google.com/scholar?q=Nizar+Habash%2C+Owen+Rambow%2C+and+Ryan+Roth%2E+2009%2E+Mada%2B+tokan%3A+A+toolkit+for+Arabic+tokenization%2C+diacritization%2C+morphological+disambiguation%2C+POS+tagging%2C+stemming+and+lemmatization%2E)
16. Bo Han and Timothy Baldwin. 2011. [Lexical normalisation of short text messages: Makn sens a #twitter](https://md.research.elamur.ai/free/en/papers/han-2011-lexical-normalisation-short-text-messages/). In Proceedings of ACL: HLT.
17. Akshay Java, Xiaodan Song, Tim Finin, and Belle Tseng. 2007. Why we Twitter: Understanding microblogging usage and communities. In Proceedings of WebKDD. ACM. [find](https://scholar.google.com/scholar?q=Akshay+Java%2C+Xiaodan+Song%2C+Tim+Finin%2C+and+Belle+Tseng%2E+2007%2E+Why+we+Twitter%3A+Understanding+microblogging+usage+and+communities%2E+In+Proceedings+of+WebKDD%2E+ACM%2E)
18. Ahmed Mourad and Kareem Darwish. 2013. [Subjectivity and sentiment analysis of modern standard Arabic and Arabic microblogs](https://md.research.elamur.ai/free/en/papers/mourad-2013-subjectivity-sentiment-analysis-modern-standard/). In Proceedings of WASSA. ACL.
19. Suleiman H Mustafa. 2012. Word stemming for Arabic information retrieval: The case for simple light stemming. [find](https://scholar.google.com/scholar?q=Suleiman+H+Mustafa%2E+2012%2E+Word+stemming+for+Arabic+information+retrieval%3A+The+case+for+simple+light+stemming%2E)
20. Eshrag Refaee and Verena Rieser. 2014. [An Arabic Twitter corpus for subjectivity and sentiment analysis](https://md.research.elamur.ai/free/en/papers/refaee-2014-arabic-twitter-corpus-subjectivity-sentiment/). In Proceedings of LREC.
21. Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011. [Named entity recognition in tweets: An experimental study](https://md.research.elamur.ai/free/en/papers/ritter-2011-named-entity-recognition-tweets-experimental/). In Proceedings of EMNLP.
22. Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. 2003. [Feature-rich part-of-speech tagging with a cyclic dependency network](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/). In Proceedings of NAACL.
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*Updated 1 November 2026*