[Feature Request]: Apache HOP Plugin for Taxonomy-Based Text Extraction and Labelling

### What would you like to happen?

An Apache HOP plugin that utilises a predefined taxonomy (a categorisation scheme) and automatically extracts and classifies snippets of text around key terms. The plugin would capture a specified number of words around the term, rounded to complete sentences, and label the text according to the taxonomy.

Use Case:
The plugin will be useful for users who need to focus on specific sections of large documents, such as known bias terms, industry-specific terms, product names, or other key phrases. Instead of manually reviewing entire documents, the plugin automatically extracts and labels relevant text segments. This plugin will facilitate semi-supervised learning by using preliminary labelled data to guide the analysis of unlabelled data.

### Issue Priority

Priority: 3

### Issue Component

Component: Transforms

No files, tests, or entry points are named. Start by reviewing Apache Hop’s existing transform and plugin conventions, then clarify how the predefined taxonomy, context-word count, sentence boundaries, and labels should be configured; done means those requirements are implemented as a usable transform.

Written by the indexing model from the issue text.