How to Extract Entities from TextRazor

What Is TextRazor?

TextRazor is a semantic processing tool used to extract entities, topics, categories, and Wikipedia-backed IDs from raw text. It mimics how Google’s NLP layer might interpret and categorize your content.

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Unlike keyword-based models, TextRazor builds a conceptual understanding by identifying entity-relation-attribute structures directly from body content.

Why Use TextRazor in Semantic SEO?

Feature	SEO Benefit
Entity Linking	Maps to Wikipedia IDs (strong contextual signals)
Topic Categorization	Shows content topicality (like Google’s topic layers)
Confidence Scoring	Prioritizes strongest entities per document
Multi-source Analysis	Combine Wikipedia + Competitor Data for topical depth
Free API & UI	Accessible to all SEOs and content strategists

Step-by-Step: Entity Extraction with TextRazor

1. Collect Raw Text

Sources to extract from:

Wikipedia page of your main topic (e.g. Plumbing)
Top-ranking competitors
Internal service/blog content

2. Paste Content into TextRazor UI

Use TextRazor Demo

Click Analyze

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3. Entity Extraction Begins

You will get:

List of entities (with Wikidata URLs)
Entity Types (Person, Location, Consumer Good, etc.)
Relevance Scores (Confidence and Salience)
Disambiguated terms (connected to Knowledge Graph)

4. Build Your Entity Sheet (Recommended Columns)

Entity	Type	Wikipedia Link	Relevance Score	Use In Content? (Y/N)
Plumbing	Thing	/wiki/Plumbing	0.98	Y
Pipe	Thing	/wiki/Pipe_(material)	0.95	Y
Water Heater	Consumer Good	/wiki/Water_heater	0.92	Y

Combine With Google NLP Output

NLP Tool Output	TextRazor Output
Entity Salience	Entity Confidence
Sentiment Score	Topic Category
ACRP Classification	Wikipedia ID

Use both tools together to triangulate your entity list and improve semantic comprehensiveness.

Why Wikipedia-Linked Entities Matter

Google’s Knowledge Graph is heavily influenced by:

Wikidata
Wikipedia entity IDs
Structured relationships between concepts

So if you include:

Entities from Wikipedia
With proper attribute-value pairs
In contextually coherent sentences

Google can parse and index the content faster and with more confidence.

Conceptual Example: E-A-V with TextRazor Entities

Let’s assume we write about Plumbing:

Entity	Attribute	Value
Water Heater	Energy Source	Electric
Pipe	Material	PVC
Plumber	Certification	Journeyman License
Sink	Installation Type	Undermount

These E-A-V triples form the semantic backbone of a well-optimized content block.

Practical Application: Article Structuring

Topical Sectioning using high-score entities
Subheading Logic aligned with entity groups
Attribute-value Injection per paragraph
Internal Linking among pages that mention shared entities
Schema Markup enriched with extracted entities (JSON-LD)

Warning: Post-Extraction Cleanup

Not all terms are useful.
Clean manually:
- Remove generic nouns (e.g. “illustration”, “talk”)
- Exclude brand mentions or UI terms (e.g. “logo”, “background”)

Tools Stack for Entity Extraction & Optimization

Tool	Purpose
Google NLP	Entity salience, sentiment, category
TextRazor	Wikipedia ID, topic linkage
Wikipedia	Source corpus for seed entity mining
Competitor Sites	Contextual discovery of industry usage
Google Sheets	Manual deduplication + segmentation

Advanced Use Case: Topical Authority via Entity Graph

Extract 200+ entities from:
- Primary Topic
- Supporting Subtopics
- Competitor Pages
Cluster them by:
- Type (Tool, Job Role, Process)
- Attribute Groups
- Search Intent
Map them to URL structure + Internal Links

This builds semantic cohesion, which boosts crawl efficiency, entity salience, and ranking probability.

Coming in Part 20: How Google Detects Entities Using NLP – Part 20

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