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

The task of predicting the next word in a sentence

Answer

The process of removing proper nouns from text

Answer

A method for measuring sentence similarity

Question 2

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

Accepted Answer

BIO (Beginning, Inside, Outside) tagging

Answer

POS (Part-of-Speech) tagging

Answer

Dependency labeling

Answer

Chunking with IOB2 only

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

Extracting noun phrases from a parse tree

Answer

Finding synonyms for words in a sentence

Answer

Splitting compound sentences into simple ones

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

Resolving spelling errors in a document

Answer

Linking a word to its definition in a dictionary

Answer

Parsing the dependency structure of a sentence

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

Counting the frequency of verbs in a document

Answer

Detecting grammatical errors in sentences

Answer

Summarizing a document into a single sentence

Question 6

Which evaluation metric is most commonly used for NER tasks?

Accepted Answer

F1 score computed at the entity span level

Answer

BLEU score

Answer

Perplexity

Answer

Mean Reciprocal Rank (MRR)