--- title: "An Amharic Stemmer : Reducing Words to their Citation Forms" description: "The paper builds a stemmer for Amharic, a Semitic language with rich morphology. 12 references, cited by 3 papers here." authors: ["Atelach Alemu Argaw", "Lars Asker"] year: 2007 venue: "Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources" source: "https://aclanthology.org/W07-0814/" 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/argaw-2007-amharic-stemmer-reducing-words-their/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/argaw-2007-amharic-stemmer-reducing-words-their/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2007](https://md.research.elamur.ai/free/en/papers/2007/page/2/) / An Amharic Stemmer : Reducing Words to their Citation Forms # An Amharic Stemmer : Reducing Words to their Citation Forms **Authors:** [Atelach Alemu Argaw](https://md.research.elamur.ai/free/en/authors/atelach-alemu-argaw/) and [Lars Asker](https://md.research.elamur.ai/free/en/authors/lars-asker/) · **Year:** 2007 · **Venue:** [Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources](https://md.research.elamur.ai/free/en/venues/workshop-on-computational-approaches-to-semitic-languages-common-issues-and/) ([Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources 2007](https://md.research.elamur.ai/free/en/venues/workshop-on-computational-approaches-to-semitic-languages-common-issues-and/2007/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/W07-0814/) · [PDF](https://aclanthology.org/W07-0814.pdf) · [OpenAlex](https://openalex.org/W2066069889) **Citations:** 23 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/argaw-2007-amharic-stemmer-reducing-words-their/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper builds a stemmer for Amharic, a Semitic language with rich morphology. It is designed for dictionary-based cross-language IR, where query words must map to the exact citation form listed in a machine readable dictionary (MRD). Full morphological analysis is seen as unnecessary for this goal, and precision matters most. The authors wrote 65 hand-crafted rules covering affixes and allowed prefix/suffix combinations per word category. The rules generate every possible segmentation of a word, and each candidate stem is checked against three MRDs. When several candidates match, or none do, the system chooses using occurrence statistics from the 3.1M-word ENH news corpus. Stems that fail to match get a small repair, such as restoring a final vowel, and are tried again. Texts are first transliterated into SERA. Accuracy is 76.9% on news from Walta Information Center and 60% on a classic novel. This shows a clear drop under stylistic shift. The work offers a practical recipe for a low-resource language with no public morphological analyzer. ## Key points - **Rules:** 65 morphological rules, from simple affixation to legal prefix–suffix combinations per word category. - **Resources:** - The ENH corpus (about 3.1M words in roughly 10,000 articles) supplies word and affix statistics. - Three MRDs (French–Amharic, Amharic–French, English–Amharic) total about 83,000 entries but only 47,176 unique ones. - **Evaluation data:** 1503 WIC words (1000 unique) and a 470-word passage of "Fikir Iske Meqabir" (300 unique). - **WIC results by case** (76.9% overall): | Case | Share of words | Accuracy | |---|---|---| | Exactly one dictionary-matching stem | 48% | 83.75% | | Several matching stems | 36.3% | 69.1% | | No match at all | 15.7% | 73.9% | - **FIM results** (60% overall): 68.8% for a single match, 54.8% for several matches, 42.1% for no match. ## Interesting ideas - Over-generate all rule-licensed segmentations and let a lexicon of citation forms act as the verifier, so the rules can stay permissive while the dictionary supplies precision. - Use a tiered back-off: an exact dictionary match first, then a lightly repaired stem (e.g. restoring a dropped final vowel), and only then corpus statistics. - When nothing matches the dictionary, prefer the shortest stem that is attested as a standalone word in a large corpus, using attestation as cheap weak evidence. - Report accuracy separately for unambiguous, ambiguous and unmatched cases, which shows where errors really come from and what future work should target. - Test on an out-of-domain text (an older novel versus news) on purpose to measure robustness to style drift. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (tokens): The en lemmatizer could propose candidate lemmas for unseen inflected or derived words via suffix rules and keep only those found in the lexicon, logging which rule produced each one. - **I2** (tokens): Each lemma decision could record its tier, making the morphology step explainable and making low-confidence lemmas easy to flag for downstream stages. - **I3** (tokens): This could guide splitting of unknown compounds or hyphenated forms by picking pieces that occur independently in a reference corpus. - **I4** (learn): Evaluation could be bucketed by how many witness analyses agree or how many lexicon matches exist, so regressions in the hard buckets are not hidden by easy cases. - **I5** (learn): Evaluations could routinely pair in-domain sets with stylistically distant ones, such as old fables versus school texts, as a shift check. *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 - [Stockholm University](https://md.research.elamur.ai/free/en/universities/stockholm-university/) — University, Stockholm, Sweden · 21 works · 1,832 citations - [KTH Royal Institute of Technology](https://md.research.elamur.ai/free/en/universities/kth-royal-institute-of-technology/) — University, Stockholm, Sweden · 18 works · 788 citations 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works 🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🧩 Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · 405 works ## Cite Cite as: Atelach Alemu Argaw and Lars Asker. 2007. An Amharic Stemmer : Reducing Words to their Citation Forms. Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{argaw2007amharic, title = {{An Amharic Stemmer : Reducing Words to their Citation Forms}}, author = {Atelach Alemu Argaw and Lars Asker}, booktitle = {Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources}, year = {2007}, doi = {10.3115/1654576.1654594}, url = {https://doi.org/10.3115/1654576.1654594} } ``` - [← previous Antecedent Selection Techniques for High-Recall Coreference Resolution](https://md.research.elamur.ai/free/en/papers/versley-2007-antecedent-selection-techniques-high-recall/) - [next → Capturing Translational Divergences with a Statistical Tree-to-Tree Aligner](https://md.research.elamur.ai/free/en/papers/hearne-2007-capturing-translational-divergences-statistical-tree/) - [↑ back to papers of 2007](https://md.research.elamur.ai/free/en/papers/2007/page/2/) ## References 12 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Berhanou Abebe. 2004. Dictionnaire Amharique-Francais. Shama Books, Addis Ababa, Ethiopia. [find](https://scholar.google.com/scholar?q=Berhanou+Abebe%2E+2004%2E+Dictionnaire+Amharique%2DFrancais%2E+Shama+Books%2C+Addis+Ababa%2C+Ethiopia%2E) 2. Sisay Fissaha Adafre. 2005. [Part of speech tagging for amharic using conditional random fields](https://md.research.elamur.ai/free/en/papers/adafre-2005-part-speech-tagging-amharic-conditional/). In Proceedings of ACL-2005 Workshop on Computational Approaches to Semitic Languages. 3. Amsalu Aklilu. 1981. Amharic - English Dictionary. Mega Publishing Enterprise, Ethiopia. [find](https://scholar.google.com/scholar?q=Amsalu+Aklilu%2E+1981%2E+Amharic+%2D+English+Dictionary%2E+Mega+Publishing+Enterprise%2C+Ethiopia%2E) 4. Nega Alemayehu and Peter Willett. 2002. The effectiveness of stemming for information retrieval in amharic. In Short Communication. [↗](https://doi.org/10.1108/00330330310500748) 5. Saba Amsalu and Dafydd Gibbon. 2005. Finite state morphology of amharic. In Proceedings of RANLP. [find](https://scholar.google.com/scholar?q=Saba+Amsalu+and+Dafydd+Gibbon%2E+2005%2E+Finite+state+morphology+of+amharic%2E+In+Proceedings+of+RANLP%2E) 6. Abiyot Bayou. 2000. Design and development of word parser for amharic language. Masterthesis, Addis Abeba Univeristy. [find](https://scholar.google.com/scholar?q=Abiyot+Bayou%2E+2000%2E+Design+and+development+of+word+parser+for+amharic+language%2E+Masterthesis%2C+Addis+Abeba+Univeristy%2E) 7. Tesfaye Bayu. 2002. Automatic morphological analyser: An experiment using unsupervised and autosegmental approach. Masterthesis, Addis Ababa University. [find](https://scholar.google.com/scholar?q=Tesfaye+Bayu%2E+2002%2E+Automatic+morphological+analyser%3A+An+experiment+using+unsupervised+and+autosegmental+approach%2E+Masterthesis%2C+Addis+Ababa+University%2E) 8. M. Lionel Bender and Hailu Fulas. 1978. Amharic verb morphology. In East Lansing: Michigan State University, African Studies Center. [find](https://scholar.google.com/scholar?q=M%2E+Lionel+Bender+and+Hailu+Fulas%2E+1978%2E+Amharic+verb+morphology%2E+In+East+Lansing%3A+Michigan+State+University%2C+African+Studies+Center%2E) 9. M. Lionel Bender. 1968. Amharic Verb Morphology: A Generative Approach. Ph.D. thesis, Graduate School of Texas. [find](https://scholar.google.com/scholar?q=M%2E+Lionel+Bender%2E+1968%2E+Amharic+Verb+Morphology%3A+A+Generative+Approach%2E+Ph%2ED%2E+thesis%2C+Graduate+School+of+Texas%2E) 10. Yitna Firdyiwek and Daniel Yacob. 1997. System for ethiopic representation in ascii. [find](https://scholar.google.com/scholar?q=Yitna+Firdyiwek+and+Daniel+Yacob%2E+1997%2E+System+for+ethiopic+representation+in+ascii%2E) 11. Sisay Fissaha and Johann Haller. 2003. Amharic verb lexicon in the context of machine translation. In Actes de la 10e conference TALN, Batz-sur-Mer. Kesatie Birhan Tesema. Adis Abeba. YeAmarinja Mezgebe Qalat. [find](https://scholar.google.com/scholar?q=Sisay+Fissaha+and+Johann+Haller%2E+2003%2E+Amharic+verb+lexicon+in+the+context+of+machine+translation%2E+In+Actes+de+la+10e+conference+TALN%2C+Batz%2Dsur%2DMer%2E+Kesatie+Birhan+Tesema%2E+Adis+Abeba%2E+YeAmarinja+Mezge) 12. Baye Yimam. 1995. ye amargna sewasew (Amharic Grammar). EMPDA. [find](https://scholar.google.com/scholar?q=Baye+Yimam%2E+1995%2E+ye+amargna+sewasew+%28Amharic+Grammar%29%2E+EMPDA%2E) *Updated 1 November 2026*