--- title: "An Approach based on semantic trees for lexical disambiguation" description: "The authors propose a semi-supervised method. 15 references." authors: ["Laroussi Merhbene", "Anis Zouaghi", "Mounir Zrigui"] year: 2014 venue: "TALN-RECITAL 2014 Workshop RLTLN 2014 : Réseaux Lexicaux pour le TAL (RLTLN 2014 : Lexical Networks for NLP)" source: "https://aclanthology.org/W14-6702/" 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/merhbene-2014-approach-based-semantic-trees-lexical/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/merhbene-2014-approach-based-semantic-trees-lexical/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/4/) / An Approach based on semantic trees for lexical disambiguation of Arabic language using a voting procedure (Approche basée sur les arbres sémantiques pour la désambiguïsation lexicale de la langue arabe en utilisant une procédure de vote) \[in French\] # An Approach based on semantic trees for lexical disambiguation of Arabic language using a voting procedure (Approche basée sur les arbres sémantiques pour la désambiguïsation lexicale de la langue arabe en utilisant une procédure de vote) \[in French\] **Authors:** [Laroussi Merhbene](https://md.research.elamur.ai/free/en/authors/laroussi-merhbene/), [Anis Zouaghi](https://md.research.elamur.ai/free/en/authors/anis-zouaghi/) and [Mounir Zrigui](https://md.research.elamur.ai/free/en/authors/mounir-zrigui/) · **Year:** 2014 · **Venue:** TALN-RECITAL 2014 Workshop RLTLN 2014 : Réseaux Lexicaux pour le TAL (RLTLN 2014 : Lexical Networks for NLP) · **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/W14-6702/) · [PDF](https://aclanthology.org/W14-6702.pdf) ## Summary The paper addresses word sense disambiguation (WSD) for Arabic, where many words have numerous out-of-context senses and no shared evaluation benchmarks exist. The authors propose a semi-supervised method. In the supervised part, dictionary glosses for each sense are reduced to roots with the resource-free Al-Shalabi, Kanaan and Al-Serhan stemmer. An approximate substring matching algorithm (Elloumi) then finds occurrences of those roots in corpus sentences, which sorts the sentences into one cluster per sense. Each sentence becomes a binary tree rooted at the ambiguous word, with neighbours placed at depths that reflect their distance from it. The trees for a sense are merged into an n-ary "semantic tree." A new sentence's tree is matched against each sense's semantic tree, giving a weighted acyclic graph. A score built from three collocation measures (T test, MI, χ2) picks the closest sense. A voting procedure ranks and weights those measures. The reported precision and recall reach about 83%. This matters as a structured, inspectable alternative for a language with scarce WSD resources. ## Key points - Preprocessing: - Sentences are segmented on punctuation and kept only if they have more than three words. - Stop words are removed with a general list of 29,985 entries compiled by Arabic linguists. The authors chose a general list over a corpus-derived one, following an earlier IR comparison. - Root extraction: - Each letter gets a weight between 0 and 5, using six letter groups. - Each letter also gets a rank that depends on word length and parity. - The three letters with the smallest weight × rank product form the root. The stemmer's reported accuracy is about 90%. - Speeding up matching: approximate matching is slow, so found root occurrences are stored in a knowledge base and checked before the algorithm is rerun. - Per-word F-scores for the most frequent sense: - Ain 0.7421 - Hsb 0.7532 - Fjr 0.8420 - Chaar 0.8926 - nr 0.9605 - Trends in the results: - Words with more senses score lower. - χ2 is the top-ranked collocation measure for most of the test data. - Overall precision and recall reach about 83%. ## Interesting ideas - Encode each context as a tree whose depth equals distance from the target word, then merge all contexts of one sense into a single weighted sense profile; proximity is thus built into the structure rather than into a flat feature list. - Several association statistics (T test, MI, χ2) score the same match, and a voting step decides how much each one counts instead of trusting a single metric. - Sense clusters are seeded automatically by finding corpus sentences that contain roots from each sense's dictionary gloss, so sense-labelled examples come without manual annotation. - Results that are costly to compute (fuzzy root matches) are memoised in a persistent knowledge base, which is consulted before falling back to the expensive algorithm. - Evaluation is broken down per ambiguous word and tied to its number of senses, which shows where the method fails instead of reporting only one average. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (frames): A per-sense profile keyed on dependency distance (not linear distance) from a verb could help choose among VerbNet classes in an inspectable way. - **I2** (learn): The same pattern fits witness-parser agreement, where each witness earns a learned weight per phenomenon rather than a fixed equal vote. - **I3** (learn): Gloss lemmas from WordNet could harvest silver sense-tagged sentences to train or check sense choice in the world ontology. - **I4** (tokens): A persistent cache of resolved lemma or morphology lookups for rare or misspelled forms would keep the slow fallback path explainable and fast. - **I5** (answer): Reporting accuracy by item difficulty (for example, the number of candidate readings in a test question) would expose weak spots hidden by aggregate scores. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🖥️ 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 🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [French](https://md.research.elamur.ai/free/en/topics/french/) · 70 works 💡 Concepts (1): [💡 Stemming](https://md.research.elamur.ai/free/en/concepts/stemming/) · 17 works ## Cite Cite as: Laroussi Merhbene, Anis Zouaghi and Mounir Zrigui. 2014. An Approach based on semantic trees for lexical disambiguation of Arabic language using a voting procedure (Approche basée sur les arbres sémantiques pour la désambiguïsation lexicale de la langue arabe en utilisant une procédure de vote) \[in French\]. TALN-RECITAL 2014 Workshop RLTLN 2014 : Réseaux Lexicaux pour le TAL (RLTLN 2014 : Lexical Networks for NLP). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{merhbene2014approach, title = {{An Approach based on semantic trees for lexical disambiguation of Arabic language using a voting procedure (Approche basée sur les arbres sémantiques pour la désambiguïsation lexicale de la langue arabe en utilisant une procédure de vote) [in French]}}, author = {Laroussi Merhbene, Anis Zouaghi and Mounir Zrigui}, booktitle = {TALN-RECITAL 2014 Workshop RLTLN 2014 : Réseaux Lexicaux pour le TAL (RLTLN 2014 : Lexical Networks for NLP)}, year = {2014}, url = {https://aclanthology.org/W14-6702/} } ``` - [← previous Alliteration in Modern and Middle English: “Piers Plowman”](https://md.research.elamur.ai/free/en/papers/sutton-2014-alliteration-modern-middle-english-piers/) - [next → An Expert System for Automatic Reading of A Text Written in Standard Arabic](https://md.research.elamur.ai/free/en/papers/hanane-2014-expert-system-automatic-reading-text/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/4/) ## References 15 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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