Document Extraction › Visualizations

Michael BrenndoerferJanuary 9, 20264 min read
Language AI HandbookAll chapters
Part 20 of 67Data Curation
Chapter 140Document Extraction › Visualizations
Reading section 10 of 13 · Visualizations · 4 minSection 10 of 13 · 4 min

Book contents

Language AI Handbook67 parts · 513 chaptersView the whole book →
Active chapter 140 of 513

Section 10 of 13 · 4 min read

Document Extraction › VisualizationsOne section · Sections in this chapter10 / 13 ↓
Sections in this chapter13
Reading tools Definitions & inline links

Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.

Article links

Make inline references clickable

Let's visualize how text density varies across page regions, which is the core signal used by most extraction algorithms:

Out[45]:
Visualization
Bar chart comparing text density and link density across nav, heading, content, sidebar, and footer regions.
Text density and link density for different regions of a typical web page. Content paragraphs have high text density (many characters per tag) and low link density (few anchor characters). Navigation and footer regions show the opposite pattern, giving a clear signal for boilerplate detection algorithms.
Out[46]:
Visualization
Two-panel bar chart comparing three extraction approaches: retained word counts on the left and tracked boilerplate marker counts on the right.
Word count retained and six keyword-based boilerplate markers across Trafilatura, rule-based BS4, and naive extraction applied to the same test document. Both structured extractors remove all six tracked markers in this example, while Trafilatura retains fewer words. Naive extraction retains the most text and all tracked markers. The marker count is a targeted diagnostic rather than a general extraction-quality score.
Out[47]:
Visualization
Line chart showing percentage of documents retained after each stage of the extraction pipeline from raw crawl to final corpus.
Cumulative document retention through successive stages of a document extraction and quality filtering pipeline. Each stage reduces the corpus size while increasing quality. The largest drops occur at content extraction (many pages yield no usable text) and the length filter (short pages are discarded), with smaller but consistent drops at each subsequent quality stage.

The pipeline retention chart illustrates a pattern common to all large-scale data curation efforts: the vast majority of the filtering work happens early, in the extraction and basic quality filtering stages. The remaining stages apply progressively finer-grained selection criteria to the already-filtered subset.