BUILD · COMPUTATIONAL LINGUISTICS

NLP Builder

Turn linguistic phenomena into computational modelsBuild the research logic before choosing code or a model. LinguaLab shows how a linguistic concept becomes annotation, structured data, a computational task, and an evaluation plan.

Research prototype

This workspace produces a planned design and illustrative representations. It does not train a model, validate annotations, or report experimental results.

Data source: Standalone input — current project data will not be changed

Choose what you want to build

Define the linguistic phenomenon

Choose the linguistic concept before choosing the method. A custom phenomenon should include its theoretical scope.

Morphology

Syntax

Semantics

Discourse

Pragmatics

Text classification

Information extraction

Other or custom

Smart Algorithm Guide

Current stage: Phenomenon

Domain: information-extraction
Unit: token
Labels: PER · ORG · LOC
Status: current-or-reviewed-step

What are we doing now?: Select the linguistic concept before choosing a computational method.
Why is this necessary?: Each decision constrains the annotation, data structure, task, and later evaluation.
What must the researcher decide?: Choose the option that matches the research question, not the most complex technology.
What happens next?: The next step will formalize another part of the same design without discarding this choice.

Design checks

No current issue was detected at this stage. Continue reviewing; this is not a validity guarantee.

What does this mean?

Start from a linguistic definition, not a preferred model.

Live Blueprint

Phenomenon: Named entities
Unit: Token
Labels: PER · ORG · LOC
Task: Token classification and named entity recognition
Algorithm family: Rule-based
Evaluation: Precision · Recall · F1
Status: Planned design

Researcher review required

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