--- title: "MUHIT: A Multilingual Harmonized Dictionary — Alansary 2014" description: "The paper describes MUHIT, a free web dictionary that brings together the vocabulary of more than 40 languages." authors: ["Sameh Alansary"] year: 2014 venue: "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)" source: "https://aclanthology.org/L14-1474/" 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/papers/alansary-2014-muhit-multilingual-harmonized-dictionary/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/alansary-2014-muhit-multilingual-harmonized-dictionary/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2014](https://md.research.elamur.ai/free/papers/2014/page/2/) / MUHIT: A Multilingual Harmonized Dictionary # MUHIT: A Multilingual Harmonized Dictionary **Authors:** Sameh Alansary · **Year:** 2014 · **Venue:** Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) · **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/L14-1474/) · [PDF](https://aclanthology.org/L14-1474.pdf) · [OpenAlex](https://openalex.org/W2251790779) **Citations:** 5 citations (openalex-snapshot · 2026-10-14) ## Summary The paper describes MUHIT, a free web dictionary that brings together the vocabulary of more than 40 languages. It gives particular attention to Arabic. The author argues that a multilingual lexical database is better built around language-independent meanings than through pairwise links between words of different languages. MUHIT was therefore created within the Universal Networking Language (UNL) framework, which the UNDL Foundation developed together with the Bibliotheca Alexandrina. Each word form is linked to a universal concept identifier (UW). This makes it possible to find synonyms and equivalents both within one language and across languages. The method combines two approaches: a WordNet-based approach and a corpus-based approach. Senses from English WordNet 3.0 were mapped to Arabic headwords, and the most frequent lexemes were taken from the ICA corpus. Entries were entered through the UNLarium web environment. UNLarium applies multi-level quality control and a unified tagset of morphological, syntactic and semantic features. The resulting database contains more than 10 million word forms, and about one fifth of them are Arabic. The resource is useful for machine translation, for cross-lingual synonym search, and as a model of harmonized lexical description. ## Key points - Size: more than 10 000 000 word forms in more than 40 languages. Arabic comes first with 2 332 765 forms, or about 20% of the database. It is followed by Russian (1 585 693), Spanish (1 374 495) and English (398 304). Some languages have only a token presence: Abkhaz has 1 form, Lao 30, and Hindi 67. - Sources for Arabic: English WordNet 3.0 has 117 659 senses, and linking them yielded about 117 000 new Arabic words. The 50 000 most frequent lexemes were taken from the ICA corpus (100 million words of Modern Standard Arabic), which added about 30 000 more words. - All entries are created and edited in UNLarium. It has a hierarchy of roles from Observer to Super manager, and every entry goes through a double check: first by an editor, then by a reviser. An author's rights are downgraded if more than 10% of their entries contain errors. - Features are described with a unified tagset aligned with the Data Category Registry (ISO 12620). Each entry specifies part of speech, gender, number, inflectional paradigms, valency, aspect, subcategorization frames and animacy. The WordNet ontology serves as the semantic classification. - Search is possible by lexeme, word form, pattern (wildcard) or concept. Inflected forms are not stored but generated from paradigms. Results show translations into other languages and synonyms in the same language. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/topics/part-of-speech-tagging/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alansary2014muhit, title = {{MUHIT: A Multilingual Harmonized Dictionary}}, author = {Alansary, Sameh}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, doi = {10.63317/5eborksg8dvw}, url = {https://doi.org/10.63317/5eborksg8dvw} } ``` - [← previous Modelling Sarcasm in Twitter, a Novel Approach](https://md.research.elamur.ai/free/papers/barbieri-2014-modelling-sarcasm-twitter-novel-approach/) - [next → N-gram Counts and Language Models from the Common Crawl](https://md.research.elamur.ai/free/papers/buck-2014-n-gram-counts-language-models/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/page/2/) ## References 12 references, 0 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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