🏷️ Named Entity Resolution (NER)

Named Entity Resolution is the process of auditing whether the recognized named entity is the entity itself or not. It is a necessary process to improve Natural Language Processing (NLP) accuracy by reducing ambiguity within the content.

🔗 Named Entity Resolution vs. Named Entity Extraction

• 🟢 Named Entity Extraction (NEE): Extracts features and attributes of entities.
• 🟢 Named Entity Resolution (NER): Determines the entity’s identity and accuracy.

Both processes complement each other for better entity understanding.

🔍 Why is Named Entity Resolution Important?

Named Entity Resolution helps Semantic Search Engines and other systems that interpret human language. It increases confidence in understanding the context and topic of a document.

📌 Example of Named Entity Resolution

Consider the sentence:

“Barry Schwartz entered the classroom and asked questions to students about human nature and thinking skills.”

• 👉 Named Entity Resolution tries to identify who "Barry Schwartz" is.
• 👉 Words like “classroom,” “questions,” “students” are contextually linked using:
  • 📌 Onasmatics
  • 📌 Semantic Role Labeling
  • 📌 Semantic Annotations

🔎 Inference: Based on this context, Barry Schwartz is likely a teacher or an academic. A search engine or NLP system can use these features to disambiguate entities accurately.

🧠 Lexical Semantics & Entity Understanding

In Lexical Semantics:

• ✔️ "Teacher" is a hypernym (broader term) of "Academic".
• ✔️ Every academic is a teacher, but not every teacher is an academic.

For Named Entity Recognition (NER), Named Entity Resolution (NER), and Named Entity Extraction (NEE), the following techniques are used:

• 📌 Semantic Annotations
• 📌 Lexical Semantics
• 📌 Onasmatics

📊 Entity Query Template Example

🔹 Google’s entity query template (by Andrew Houge) demonstrates how an entity in a query can have specific attributes. This helps search engines better understand search intent and provide accurate results.