Our Solutions
Our Methodology
We apply a dual methodology to linguistic preservation: high-resolution digitization of fragile manuscripts combined with modern NLP techniques including morphological analysis, named entity recognition, and text normalization for non-standardized orthographies. These tools accelerate pattern extraction and corpus annotation, tasks traditionally requiring years of manual effort.
Principled Approach
All datasets are human-supervised. Automated annotations are cross-referenced against verified historical corpora and reviewed by trained linguists to minimize errors introduced by statistical models. Language is not data alone; it is context, memory, and structure. Our computational approach serves human scholarship, not the reverse.
Cross-Temporal Analysis
Our systems enable alignment of orthographic variants across historical periods, allowing researchers to trace spelling changes and identify cognates in dynamically searchable environments. We employ lexical semantic change detection methods to identify meaning drift across centuries within language families, quantifying shifts that comparative philology has long studied qualitatively.
Tools for Discovery
Our goal extends beyond preservation to empowerment. We provide researchers and communities with structured, searchable corpora and analytical tools, turning decades of archival material into foundations for immediate scholarly inquiry and language teaching initiatives.
The Tangible Asset
At the heart of our initiative is the curation of unique, high-value linguistic datasets. These range from annotated speech corpora to cross-linked lexical databases derived from disparate historical texts. Each asset is designed not merely for storage, but for active , computational research.
Commitment to Human Expertise
While automation accelerates access, we remain committed to expert review. Every transcription, every morphological parse, and every proposed reconstruction is reviewed and refined by linguists. We do not automate interpretation; we provide tools that make human interpretation more efficient.
A Framework for Computational Language Documentation
The Corpus
Our process begins with gathering linguistic data from at-risk languages. We compile materials from diverse sources: historical manuscripts, academic grammars, lexicons, and, where possible, field recordings from fluent speakers.
Phonological Analysis
We move beyond text by incorporating acoustic data. Using automatic speech recognition (ASR) adapted for low-resource settings, we generate time-aligned transcriptions and extract phonetic inventories, including suprasegmental features like tone and stress patterns.
Grammatical Documentation
Our annotation pipelines identify morphological structure: roots, affixes, clitics, and inflectional paradigms. For languages with limited data, we employ cross-lingual transfer from related languages using multilingual models (mBERT, XLM-R) to bootstrap initial annotations.
The Computational Model
Where sufficient data exists, we train language-specific models for tasks like morphological analysis, part-of-speech tagging, and text prediction. These models encode grammatical patterns learned from the corpus, useful for consistency checking and pedagogical applications.
The Living Archive
Each documented language is housed in a searchable digital archive, accessible to researchers, educators, and descendant communities. Data sovereignty remains with source communities; access terms are defined collaboratively.
Rebuilding Memory
“Languages are not just data points; they are the lifeblood of a culture. Where others see fragments, we see structure waiting to be documented. We don’t just store language; we make it accessible for those who will carry it forward.”
