Textual Imputation
Overview
Textual Imputation is a Hellixia tool that converts free‑text comments (e.g., product reviews) into structured numerical data that a Bayesian Network can learn from. It is typically the second step in the LLM‑Text Driven Machine Learning workflow, after Textual Dimension Elicitor has defined the propositions.
Workflow
For each comment and each propositional node, Textual Imputation calculates a continuous value between 0 and 1, called the Evocation Score. A score of 0 means the proposition is not evoked at all; 1 means it is evoked unambiguously.
For every propositional node, a new continuous column is added to BayesiaLab’s Internal Dataset. This column holds the raw evocation scores, making the LLM’s textual judgments available as structured numeric data.
By default, each propositional node’s states are and , obtained by discretizing the evocation score at a threshold of 0.5. If the score > 0.5, the comment is considered to evoke the proposition; otherwise, it does not.
The resulting propositional nodes and the associated (now complete) data can be used by any BayesiaLab machine learning algorithm – such as EQ, Taboo Search, or supervised learning. Text‑derived variables enter the same modeling pipeline as ordinary numeric or categorical variables.
In essence, Textual Imputation bridges unstructured text and quantitative modeling by having an LLM judge how strongly each comment relates to pre‑defined propositions, then writing those judgments as usable columns in the dataset. It is one form of LLM‑driven Data Imputation.