MAPS (Orthography)

Arabic Verb Conjugator

As a non-concatenative, derivational language, Arabic relies on intricate morphotactics where inflection is driven by affixation—attaching morphemes without altering the root consonants or breaking the strict core order of the verb binyanim. This desktop application leverages these highly regular structural patterns to deliver an exhaustive, automated inflection generator built for linguistic research and advanced computational applications.

Driven by a pure root-based algorithm, this high-performance lexical production module generates immense morphological depth from minimalist inputs. For instance, seeding a single triconsonantal sound root like [ksr] ("to break") empowers the system to instantly compute and produce roughly 30,000 distinct conjugations—a scalability that applies across the entire paradigm of standard three-letter roots.

Rather than providing a static, pre-compiled index of verbs, this software acts as a dynamic engine capable of reconstructing the entire inflectional model of the Arabic language or generating targeted, highly granular conjugation tables on demand. Optimized for integration, compiled binary scripts are available for direct implementation into proprietary workflows.

Arabic Noun Inflector

This specialized desktop software automates the process of Arabic noun declension, accurately inflecting individual nouns across more than a dozen distinct grammatical categories. The high-performance engine seamlessly handles core structural derivations—including Verbal Nouns, Nouns of Instrument, Active Participles, Passive Participles, Locative Nouns, and Numerative Nouns—while precisely applying the three structural Arabic cases: Accusative, Nominative, and Genitive.

By treating the initial group of categories as direct derivatives of their parallel verbs, this linguistic module ensures strict grammatical accuracy across complex sentences. To maximize processing speed and consistency, stem-inherent and generic properties like semantic classifications are directly hard-coded into the core declension algorithms, bypassing the need for heavy external databases.

Arabic Root Extractor

As a highly inflectional language, Arabic relies on a deeply generative system to derive words. While standard stemming simply strips superficial affixes, this full-fledged morphological analyzer employs a high-performance light stemmer engineered to execute precise root extraction. The local desktop application incorporates advanced algorithmic pattern recognition to effectively deconstruct complex, non-concatenative structures, seamlessly resolving corrupted, assimilated, hollow, and defective tokens back to their true base forms.

By accurately isolating and returning the correct root or stem, the software eliminates the ambiguities common in structural Arabic text processing. To maximize accuracy and speed, a comprehensive, built-in root dictionary is integrated directly into the engine, making this desktop application an indispensable tool for Arabic monolingual document retrieval, indexing, and text-mining pipelines.

Arabic Text Diacritizer

As a principal UN official language with a rich vocabulary and complex morphology, Arabic features a unique script where twenty-five of its twenty-eight letters represent consonants and three denote long vowels. Because standard written Arabic completely lacks letters for short vowels, these phonetic markers are traditionally represented by short strokes called diacritics placed above or below the preceding consonants. In modern digital communication, business documentation, and news media, text is almost exclusively written unvocalized (without diacritics), creating a massive stumbling block for natural language processing systems, machine learning models, and automated text readers.

This high-performance desktop module directly solves this structural barrier by delivering an intelligent, automated text restoration engine. Developed to accommodate diverse data workflows, the software executes precise full-vocalization or semi-vocalization of raw, unvocalized input text. By dynamically predicting the correct placement of short vowels based on contextual and morphological analysis, this tool ensures absolute phonetic clarity, making it an essential pre-processing module for Text-to-Speech (TTS) engines, machine translation, and language learning applications.

Arabic Stemmer

This upcoming desktop module is currently in active development to bring rapid, lightweight affix-stripping capabilities to text pre-processing pipelines. Engineered to complement our deep morphological analysis tools, this specialized stemmer will isolate base word forms by efficiently removing prefixes, suffixes, and inflections from raw Arabic text tokens.

Please refer back to this section regularly or subscribe to our release notes for development milestones, beta testing opportunities, and the official deployment schedule.