EGlossa

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Machine Learning
for Linguistic Annotation

Learn to leverage ML techniques for automated annotation, model training, and evaluation in modern linguistic research workflows.

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Model Training Interface

[Interactive ML training visualization]

Core Concepts

Supervised Learning

Train models using annotated corpora with labeled dependencies, relations, and features.

Evaluation Metrics

F1-scores, precision-recall analysis, and cross-validation techniques for accurate model assessment.

Step-by-Step Tutorial

1. Data Preparation

Curate annotated training data from the EGlossa corpus library. Use the framework converter to standardize formats.

2. Feature Engineering

Extract syntactic patterns, contextual embeddings, and linguistic features using the preprocessor.

3. Model Training

Configure hyperparameters in the JSON settings file and run a training session with framework support for CRF, RNN, Transformer models.

4. Evaluation

Run validation on development sets to analyze model performance via the evaluation dashboard.

Code Example

{1} // Sample configuration {2} const config = { {3} framework: "transformer", {4} max_epochs: 15, {5} learning_rate: 0.001, {6} batch_size: 32, {7} evaluation_metric: "f1_macro" {8} };

Practical Examples

Named Entity Recognition Demo

This model tags person names, locations, and organizations in text corpora.

Input: "John Smith worked at Microsoft in Redmond, WA."
Output: ["B-PER", "I-PER", "O", "B-ORG", "O", "B-LOC", "O", "O"]
Try your own text in the annotation dashboard for interactive results