---
title: Semantic Cohesion and Resolving Meaning Gaps
description: "### **The Challenge** Texts overloaded with keywords (Keyword Stuffing) are easily detected and penalized by NLP algorithms. It’s crucial not only to include keywords but to connect them naturally. ### **The Solution** - **Proximity Analysis:** The platform measures the semantic…"
url: "https://scrizen.com/docs/deep-semantics/semantic-cohesion-and-resolving-meaning-gaps/"
updated: "2026-03-29"
category: "Deep Semantics & Analytics"
---

# Semantic Cohesion and Resolving Meaning Gaps

### **The Challenge**

Texts overloaded with keywords (Keyword Stuffing) are easily detected and penalized by NLP algorithms. It’s crucial not only to include keywords but to connect them naturally.

### **The Solution**

- **Proximity Analysis:** The platform measures the semantic distance between related phrases. The application calculates the distance between keywords directly through the site's structure (DOM tree).
- **Virtual Penalties:** If keywords are separated by a period, a paragraph break, or an HTML block transition, the algorithm increases the semantic distance. The system accounts for transitions between blocks and applies multiple types of penalties (hard and soft breaks: from punctuation and brackets to external links).
- **Hard Breaks:** If an external link stands between them, the connection is severed (Hard Break).
- **Semantic Issues (Diagnostics):** The platform automatically scans the text in a natural reading order (left-to-right) and highlights anomalies where logic sags, semantic connections are broken, and keywords are placed too far apart.

You receive a clear Action Plan to improve content readability and cohesion.