Introduction to Natural Language Processing (i-NLP) · Quiz 1 · Jan 2025

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highest

None of these       **i-NLP** **Section Id :** 64065379955 **Section Number :** 8 **Section type :** Online **Mandatory or Optional :** Mandatory **Number of Questions :** 31 **Number of Questions to be attempted :** 31 **Section Marks :** 50 **Display Number Panel :** Yes **Section Negative Marks :** 0 **Group All Questions :** No **Enable Mark as Answered Mark for Review and** No **Clear Response :** **Section Maximum Duration :** 0 **Section Minimum Duration :** 0 **Section Time In :** Minutes **Maximum Instruction Time :** 0

Lexical

Discourse

Pragmatic

Syntactic

Yes

No

The presence of words with ambiguous lexical categories that can lead to multiple syntactic structures.

Difficulty in identifying compound nouns within the sentence.

The sentence contains idiomatic expressions that require semantic understanding.

The use of archaic word forms.

Yes

No

FSAs are designed solely for recognizing patterns in input strings, while FSTs can recognize patterns and produce corresponding outputs.

Both FSAs and FSTs utilize a single tape for processing input, but FSAs have a fixed number of states while FSTs can dynamically change the number of states during execution.

FSTs can be used to perform computations involving multiple input tapes, whereas FSAs are limited to a single input tape.

FSA can produce outputs based on their input states, while FSTs are only capable of recognizing inputs without generating outputs.

Calculate state probabilities → Update observation probabilities → Compute final sequence probability

Initialize state probabilities → Calculate joint probabilities → Sum over all possible paths

Calculate emission probabilities → Sum transition probabilities → Normalize final states

Initialize α1(i) → Calculate αt(i) recursively → Sum over final states

Current word and its immediate next word

Word length and first character capitalization

Previous tag and current word ending

Current word’s morphological features combined with a window of ±2 words and their POS tags

1-B, 2-C, 3-A, 4-D

1-A, 2-B, 3-C, 4-D

1-B, 2-A, 3-D, 4-C

1-D, 2-C, 3-B, 4-A

1.5

1.6

1.7

1.8

UAS: 90%, LS: 95%

UAS: 90%, LS: 85%

UAS: 85%, LS: 95%

UAS: 85%, LS: 90%

Yes

No

Yes

No

Lemmatization

Stemming

Full morphological analysis

Manual word reduction

Stemming

Lemmatization

Simple suffix removal

No word reduction

Yes

No

Albert Einstein and Theory of Relativity

Amazon and Amazon River

Tesla and Model S

Shakespeare and Hamlet

It allows for multiple consecutive adjectives before the noun.

It requires exactly one determiner before any adjectives.

It permits zero or more prepositional phrases after the noun.

The noun is the only mandatory element in this noun phrase.

Agent: Chef, Theme: Meal, Beneficiary: Guests, Instrument: Ingredients

Agent: Meal, Theme: Guests, Beneficiary: Chef, Instrument: Cooking

Agent: Chef, Theme: Ingredients, Beneficiary: Meal, Instrument: Guests

Agent: Ingredients, Theme: Chef, Beneficiary: Meal, Instrument: Cooking

Yes

No

Yes

No

Yes

No

Precision

Recall

F1 Score

Accuracy

0.0090

0.036

0.0045

0.0036

Grouping similar entities in NER

Unsupervised POS tagging

Sentiment analysis

Word clustering into syntactic categories

Machine translation

MA is independent of syntactic rules, while POS tagging often depends on them

MA may produce multiple analyses for a word while POS tagging produces one

MA requires fewer categories compared to POS tagging

MA assigns functional roles in a sentence, while POS tagging deals with form and structure.

Over-stemming

Under-stemming

Requiring a large dictionary

Creating non-words

Both POS tagging and NER can be framed as classification problems

Naive Bayes and logistic regression can be applied to both tasks

Clustering can be used as a preprocessing step for both tasks

POS tagging always precedes NER in NLP pipelines

Both tasks benefit from considering contextual information

Generating a coherent multi-paragraph story

Summarizing a long document

Resolving pronouns in a conversation

Determining the sentiment of a single word

Identifying the topic shifts

**Stemming** can result in non-linguistic root forms, while **lemmatization** always produces valid words.

**Lemmatization** requires a vocabulary or lexicon, whereas **stemming** does not.

**Stemming** is computationally more expensive than **lemmatization** because it involves morphological analysis.

Both **stemming** and **lemmatization** aim to reduce words to their root forms but differ in their approaches and accuracy.