Natural Language Processing Named Entity Recognition and Information Extraction

Question 1

What is Named Entity Recognition (NER) in NLP?

Accepted Answer

The task of locating and classifying named entities in text into predefined categories such as person, organization, or location

Answer

NER identifies spans of text that refer to real-world entities and categorizes them, e.g., tagging 'Apple' as an organization and 'Cupertino' as a location.

Question 2

Which tagging scheme is commonly used for NER to mark entity boundaries?

Accepted Answer

BIO (Beginning, Inside, Outside) tagging

Answer

BIO tagging marks the first token of an entity with B-, continuation tokens with I-, and non-entity tokens with O, clearly delineating entity boundaries.

Question 3

What is relation extraction in NLP?

Accepted Answer

Identifying semantic relationships between entities mentioned in text, such as 'works at' or 'located in'

Answer

Relation extraction goes beyond NER to determine how entities are related, populating knowledge bases with structured (subject, relation, object) triples.

Question 4

What is coreference resolution?

Accepted Answer

Determining when two or more expressions in a text refer to the same real-world entity

Answer

Coreference resolution clusters mentions (e.g., 'Barack Obama', 'he', 'the president') that refer to the same entity, enabling coherent text understanding.

Question 5

What is event extraction in information extraction?

Accepted Answer

Identifying mentions of specific events in text and extracting their participants, time, and location

Answer

Event extraction detects trigger words and fills argument slots (who, did what, to whom, when, where) to build a structured representation of events from unstructured text.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing Named Entity Recognition and Information Extraction