--- title: "Stemming Tigrinya Words for Information Retrieval — Osman 2012" description: "Tigrinya is a Semitic language of Eritrea and Ethiopia with very few computational… 89.3% accuracy. 14 references, cited by 4 papers here." authors: ["Omer Osman", "Yoshiki Mikami"] year: 2012 venue: "Proceedings of COLING 2012: Demonstration Papers" source: "https://aclanthology.org/C12-3043/" 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/osman-2012-stemming-tigrinya-words-information-retrieval/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/osman-2012-stemming-tigrinya-words-information-retrieval/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) / Stemming Tigrinya Words for Information Retrieval # Stemming Tigrinya Words for Information Retrieval **Authors:** Omer Osman and Yoshiki Mikami · **Year:** 2012 · **Venue:** [Proceedings of COLING 2012: Demonstration Papers](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2012](https://md.research.elamur.ai/free/en/venues/coling/2012/)) · **Publisher:** [International Committee on Computational Linguistics (ICCL)](https://md.research.elamur.ai/free/en/publishers/international-committee-on-computational-linguistics-iccl/) · **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/C12-3043/) · [PDF](https://aclanthology.org/C12-3043.pdf) · [OpenAlex](https://openalex.org/W2250311666) **Citations:** 7 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/osman-2012-stemming-tigrinya-words-information-retrieval/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Tigrinya is a Semitic language of Eritrea and Ethiopia with very few computational resources. Its words carry many prefixes, suffixes and internal changes, so a plain search index treats one word as many terms. The authors built a stemmer as part of a planned Tigrinya search engine. They crawled Tigrinya web pages from news, religious, political, sports and other sites, then used the resulting word lexicon to compile affix lists, a stop-word list and a stem dictionary. The stemmer works as a pipeline: - strip apostrophe-marked clitics and punctuation; - unify letters that sound the same but are written differently; - skip stop words; - romanize the Ethiopic syllables; - remove the longest matching affixes iteratively; - collapse doubled consonants; - check the result against the stem dictionary, using edit distance and corpus frequency as fallbacks. Tested on words from a newspaper and a Bible, the stemmer averaged 89.3% accuracy. This beats an earlier rule-only Tigrinya stemmer and gives a usable building block for Tigrinya retrieval. ## Key points - The authors crawled a web corpus with a crawler built for Tigrinya content and derived a lexicon of 690,000 unique words. The corpus is released via Eri-NLP (eri-nlp.com). - They manually compiled 153 prefixes and 204 suffixes. Affix removal picks the longest match but respects a minimum stem length; words need at least three consonants to be stripped further. - Ethiopic script is romanized with SERA conventions (with a few exceptions) before stripping. This matters because suffix vowels are fused into syllable glyphs. - Dictionary verification works in three steps: - accept an exact match; - otherwise collect dictionary stems contained in the stripped string and drop those beyond a Levenshtein cutoff; - choose the closest remaining stem, breaking ties by corpus frequency. - Implemented in C#, the stemmer scored 89.92% on 1,200 Hadas Eritra words and 88.6% on 1,300 Bible words, averaging 89.3%. A prior rule-based Tigrinya stemmer reported 86%. ## Interesting ideas - Transliterating to an explicit consonant+vowel representation before stripping lets morpheme boundaries that hide inside a glyph become visible, so suffixes can be cut cleanly. - The pipeline proposes a stem with cheap rules, then accepts it only if a lexicon confirms it. Otherwise it picks the nearest contained dictionary entry under a distance cap, with frequency as the tie-breaker. - Spelling variants from regional conventions (Eritrean vs Ethiopian letter series) are merged before any other analysis, so they do not split counts or matches. - A minimum-length guard on the remaining stem is a simple, transparent brake against over-stemming short words. - The stemmer was evaluated separately on two very different genres (news vs scripture) rather than one pooled sample, which shows how stable the method is across domains. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (tokens): Run suffix analysis on a decomposed form where spelling alternations are made explicit (e.g. "-ies" ↔ "y+s"), so the boundary is not guessed on the raw surface string. - **I2** (tokens): The lemmatizer could follow the same "rule proposes, lexicon disposes" pattern for unknown words, logging which branch fired so each lemma choice stays explainable. - **I3** (tokens): Normalize British/American spellings and similar variants ahead of lemma lookup, keeping the original form for generation. - **I4** (tokens): Unknown-word suffix stripping in the en crate could refuse cuts that leave too short a base, preventing errors like turning "news" into "new". - **I5** (learn): Report per-genre scores (school texts, fables, news) for each component, so a gain in one register is not hiding a loss in another. *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 - [Nagaoka University of Technology](https://md.research.elamur.ai/free/en/universities/nagaoka-university-of-technology/) — University, Nagaoka, Japan · 2 works · 61 citations 📚 Shelves (5): [🖥️ 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 · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works 🧭 Topics (3): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works · [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works · [🧩 Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · 420 works ## Cite Cite as: Omer Osman and Yoshiki Mikami. 2012. Stemming Tigrinya Words for Information Retrieval. Proceedings of COLING 2012: Demonstration Papers. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{osman2012stemming, title = {{Stemming Tigrinya Words for Information Retrieval}}, author = {Omer Osman and Yoshiki Mikami}, booktitle = {Proceedings of COLING 2012: Demonstration Papers}, year = {2012}, url = {https://aclanthology.org/C12-3043/} } ``` - [← previous Joint English Spelling Error Correction and POS Tagging for Language Learners Writing](https://md.research.elamur.ai/free/en/papers/sakaguchi-2012-joint-english-spelling-error-correction/) - [next → Predicting Phrase Breaks in Classical and Modern Standard Arabic Text](https://md.research.elamur.ai/free/en/papers/sawalha-2012-predicting-phrase-breaks-classical-modern/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) ## References 14 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Pirkola, A. & Järvelin, K. (2001) :Morphological typology of languages for IR. Journal of Documentation, 57 (3): 330-348. [↗](https://doi.org/10.1108/eum0000000007085) 2. Ethnologue, Languages of the World (2011) : [http://www.ethnologue.com](http://www.ethnologue.com). [↗](http://www.ethnologue.com) 3. Michael Gasser. (2009). [Semitic morphological analysis and generation using finite state transducers with feature structures](https://md.research.elamur.ai/free/en/papers/gasser-2009-semitic-morphological-analysis-generation-finite/). In Proceedings of the 12th Conference of the European Chapter of the ACL, pages 309–317, Athens, Greece. 4. Anjali Ganesh Jivani et al, (2011). A Comparative Study of Stemming Algorithms , Int. J. Comp. Tech. Appl., Vol 2 (6), 1930-1938. [find](https://scholar.google.com/scholar?q=Anjali+Ganesh+Jivani+et+al%2C+%282011%29%2E+A+Comparative+Study+of+Stemming+Algorithms+%2C+Int%2E+J%2E+Comp%2E+Tech%2E+Appl%2E%2C+Vol+2+%286%29%2C+1930%2D1938%2E) 5. J. B. Lovins,(1968). Development of a stemming algorithm, Mechanical Translation and Computer Linguistic., vol.11, no.1/2, pp. 22-31. [find](https://scholar.google.com/scholar?q=J%2E+B%2E+Lovins%2C%281968%29%2E+Development+of+a+stemming+algorithm%2C+Mechanical+Translation+and+Computer+Linguistic%2E%2C+vol%2E11%2C+no%2E1%2F2%2C+pp%2E+22%2D31%2E) 6. Porter M.F (1980). “An algorithm for suffix stripping”. Program:14, 130-137. [↗](https://doi.org/10.1108/eb046814) 7. Krovetz Robert(1993). “Viewing morphology as an inference process”. Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval: 191-202. [find](https://scholar.google.com/scholar?q=Krovetz+Robert%281993%29%2E+%E2%80%9CViewing+morphology+as+an+inference+process%E2%80%9D%2E+Proceedings+of+the+16th+annual+international+ACM+SIGIR+conference+on+Research+and+development+in+information+retrieval%3A+191%2D202%2E) 8. Yasir Alhanini, Mohd Juzaiddin Ab Aziz, (2011).The Enhancement of Arabic Stemming by Using Light Stemming and Dictionary-Based Stemming, Journal of Software Engineering and Applications, 4: 522-526, ISI Wos, DBLP. [find](https://scholar.google.com/scholar?q=Yasir+Alhanini%2C+Mohd+Juzaiddin+Ab+Aziz%2C+%282011%29%2EThe+Enhancement+of+Arabic+Stemming+by+Using+Light+Stemming+and+Dictionary%2DBased+Stemming%2C+Journal+of+Software+Engineering+and+Applications%2C+4%3A+522%2D526%2C+I) 9. Sandipan Sarkar and Sivaji Bandyopadhyay,(2009). Study on Rule-Based Stemming Patterns and Issues in a Bengali Short Story-Based Corpus. In ICON. [find](https://scholar.google.com/scholar?q=Sandipan+Sarkar+and+Sivaji+Bandyopadhyay%2C%282009%29%2E+Study+on+Rule%2DBased+Stemming+Patterns+and+Issues+in+a+Bengali+Short+Story%2DBased+Corpus%2E+In+ICON%2E) 10. Yonnas Fissiha (2011):Developement of Stemming algorithm for Tigrinya, [http://etd.aau.edu.et](http://etd.aau.edu.et) [↗](http://etd.aau.edu.et) 11. Alemayehu, N., Willett, P. (2002). Stemming of Amharic words for information retrieval. Literary and Linguistic Computing 17(1), 1-17 [↗](https://doi.org/10.1093/llc/17.1.1) 12. Firdyiwek, Yitna and Daniel Yaqob. (1997). The system for Ethiopic representation in ASCII URL: citeseer.ist.psu.edu/56365.html. [find](https://scholar.google.com/scholar?q=Firdyiwek%2C+Yitna+and+Daniel+Yaqob%2E+%281997%29%2E+The+system+for+Ethiopic+representation+in+ASCII+URL%3A+citeseer%2Eist%2Epsu%2Eedu%2F56365%2Ehtml%2E) 13. V. Levenshtein (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8):707–710. [find](https://scholar.google.com/scholar?q=V%2E+Levenshtein+%281966%29%2E+Binary+codes+capable+of+correcting+deletions%2C+insertions+and+reversals%2E+Soviet+Physics+Doklady%2C+10%288%29%3A707%E2%80%93710%2E) 14. Eri-NLP (2012): Eritrean Language Research Website, URL: [http://www.eri-nlp.com/resources](http://www.eri-nlp.com/resources) [↗](http://www.eri-nlp.com/resources) *Updated 1 November 2026*