Machine Learning Safeguards Endangered Languages: A Technical Exploration of Predicting Language Endangerment Levels

This article introduces an innovative project that uses machine learning to predict the degree of language endangerment. By analyzing key features leading to language endangerment, it builds a prediction model to identify language resources requiring priority protection, addressing the issues of low efficiency and difficulty in scaling traditional manual assessments, and providing data science support for the preservation of linguistic diversity.

Language is an important carrier of human civilization; each language contains a unique worldview and cultural traditions. According to UNESCO statistics, over 40% of the world's approximately 7,000 languages are at risk of endangerment, with one language disappearing every two weeks on average. Traditional language protection relies on anthropologists' field surveys, but it is inefficient and difficult to scale when faced with massive data. The introduction of machine learning technology brings new possibilities to this field: through algorithms to automatically identify patterns of endangered features and predict the degree of endangerment, helping conservation workers prioritize resource allocation.

The core objective of this project is to explore the key features leading to language endangerment and use modern machine learning methods to predict language endangerment levels. The technical roadmap includes four stages: data collection and integration, feature engineering, model training and evaluation, and result interpretation and application recommendations. Data sources integrate multi-source information such as UNESCO's Atlas of Endangered Languages, the Ethnologue database, and census data, constructing a comprehensive dataset covering dimensions like language name, number of speakers, geographical distribution, intergenerational transmission status, official status, educational medium, and written tradition.

Through feature importance analysis, the core factors affecting the degree of language endangerment are identified:

1. Population size and trends: Languages with fewer than 10,000 speakers are at extremely high risk, and languages with a continuously declining population have a several-fold higher probability of endangerment;
2. Intergenerational transmission status: There is a strong negative correlation between children's usage rate and language endangerment level; languages no longer learned as mother tongues by children see a sharp rise in their degree of endangerment;
3. Social function and official status: Languages recognized as official by the state have stronger vitality; languages lacking educational medium functions and written traditions are more likely to die out; in addition, geographical distribution concentration, religious use, and community language attitudes also have significant predictive power.

The project attempts various algorithms such as logistic regression, random forest, gradient boosting trees (XGBoost/LightGBM), and support vector machines. After cross-validation, ensemble learning methods (random forest, gradient boosting trees) have the best predictive performance and can effectively handle non-linear feature interactions. Model evaluation uses metrics such as multi-classification accuracy, F1 score, and confusion matrix. Considering the ordinal nature of endangerment levels, ordinal regression methods are introduced to improve prediction accuracy.

The model reveals the deep mechanism of language endangerment: feature importance shows that intergenerational transmission is the strongest predictive factor, followed by population size and official status, which is consistent with the mainstream views in linguistics. Based on the results, a hierarchical conservation strategy is proposed:

• Critically endangered languages: Take emergency recording measures, prioritizing the rescue of oral traditions and language materials;
• Languages at moderate risk of endangerment: Focus on supporting community language education programs;
• Safe but potentially at-risk languages: Establish long-term monitoring mechanisms.

The project faces several technical challenges and their solutions:

1. Data sparsity: Limited accessible data for endangered languages and many missing values in the feature matrix are mitigated by using multiple imputation and similar language value inference;
2. Class imbalance: The number of samples for safe-level languages is far more than that of endangered languages, solved through SMOTE oversampling, class weight adjustment, and focal loss function;
3. Subjectivity in feature engineering: Social and cultural factors are difficult to quantify; cooperate with linguistics experts to convert qualitative assessments into structured features and retain uncertainty estimates.

This project demonstrates the potential of cross-disciplinary applications of data science in the humanities, assisting conservation workers in quickly screening languages of concern and optimizing resource allocation. Future directions include: integrating dynamic data such as social media language usage trends to develop a real-time monitoring and early warning system; building a multilingual knowledge graph to support language similarity comparison and kinship inference; developing an interactive visualization platform to show the status of global linguistic diversity to the public.