Natural Language Processing – Class 10 Artificial Intelligence NCERT Book Solutions
Que 1. What is the primary challenge faced by computers in understanding human languages?
A) Complexity of human languages
B) Lack of computational power
C) Incompatibility with numerical data
D) Limited vocabulary
Answer: A) Complexity of human languages
Que 2. How do voice assistants utilize NLP?
A) To analyze visual data
B) To process numerical data
C) To understand natural language
D) To execute tasks based on computer code
Answer: C) To understand natural language
Que 3. Which of the following is NOT a step in Text Normalisation?
A) Tokenization
B) Lemmatization
C) Punctuation removal
D) Document summarization
Answer: D) Document summarization
Que 4. In the context of text processing, what is the purpose of tokenisation?
A) To convert text into numerical data
B) To segment sentences into smaller units
C) To translate text into multiple languages
D) To summarize documents for analysis
Answer: B) To segment sentences into smaller units
Que 5. What distinguishes lemmatization from stemming?
A) Lemmatization produces meaningful words after affix removal, while stemming does not.
B) Lemmatization is faster than stemming.
C) Stemming ensures the accuracy of the final word.
D) Stemming generates shorter words compared to lemmatization.
Answer: A) Lemmatization produces meaningful words after affix removal, while stemming does not.
Que 6. What is the primary purpose of the Bag of Words model in Natural Language Processing?
A) To translate text into multiple languages
B) To extract features from text for machine learning algorithms
C) To summarize documents for analysis
D) To remove punctuation marks from text
Answer: B) To extract features from text for machine learning algorithms
Que 7. In the context of text processing, what are stop words?
A) Words with the frequent occurrences in the corpus
B) Words with negligible value that are often removed during preprocessing
C) Words with the lowest occurrence in the corpus
D) Words with the most value added to the corpus
Answer: B) Words with negligible value that are often removed during preprocessing
Que 8. What is the characteristic of rare or valuable words in the described plot?
A) They have the highest occurrence in the corpus
B) They are often considered stop words
C) They occur the least but add the most value to the corpus
D) They are typically removed during preprocessing
Answer: C) They occur the least but add the most value to the corpus
Que 9. What information does the document vector table provide?
A) The frequency of each word across all documents
B) The frequency of each word in a single document
C) The total number of words in the entire corpus
D) The average word length in the entire corpus
Answer: A) The frequency of each word across all documents
Que 10. What is the primary purpose of TFIDF in text processing?
A) To identify the presence of stop words in documents
B) To remove punctuation marks from text
C) To identify the value of each word in a document
D) To translate text into multiple languages
Answer: C) To identify the value of each word in a document
Que 11. Assertion & Reason
Assertion (A): Pragmatic analysis in natural language processing (NLP) involves assessing sentences for their practical applicability in real-world scenarios.
Reasoning (R): Pragmatic analysis requires understanding the intended meaning behind sentences and considering their practical or logical implications, rather than solely relying on literal word meanings obtained from semantic analysis.
A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
B) Assertion is true, but Reasoning is false.
C) Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
D) Assertion is false, but Reasoning is true.
Answer: A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
Que 12. Assertion & Reason
Assertion (A): Converting the entire text into lowercase following stop word removal is a crucial preprocessing step in natural language processing.
Reasoning (R): This process ensures uniformity in word representation, preventing the machine from treating words with different cases as distinct entities, thereby enhancing the accuracy of subsequent text analysis.
A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
B) Assertion is true, but Reasoning is false.
C) Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
D) Assertion is false, but Reasoning is true.
Answer: A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
Reflection Time:
Que 1. Mention a few features of natural languages.
Answer: Some features of natural languages are:
- “They are governed by set rules that include syntax, lexicon, and semantics.”
- “All natural languages are redundant, i.e., the information can be conveyed in multiple ways.”
- “All natural languages change over time.”
Que 2. What is the significance of NLP?
Answer: NLP is important because computers cannot directly understand human language.
- “Natural Language Processing facilitates this conversion to digital form from the natural form.”
- “The whole purpose of NLP is to make communication between computer systems and humans possible.”
Que 3. What do you mean by lexical analysis in NLP?
Answer: Lexical Analysis is the first stage of NLP.
It is “the process of dividing a large chunk of words into structural paragraphs, sentences, and words.”
Here, the structure of input words is identified.
Que 4. What do you mean by a chatbot?
Answer: A chatbot is “a computer program that is designed to simulate human conversation through voice commands or text chats or both.”
It can “answer questions, troubleshoot problems, generate leads, and interact like humans.”
Que 5. What does the term “Bag of Words” refer to in Natural Language Processing (NLP)?
Answer: Bag of Words is an NLP model that:
- “helps in extracting features out of the text”
- “returns the unique words of the corpus and their occurrences”
- gives vocabulary + frequency of each word
Que 6. Describe two practical uses of Natural Language Processing in real-world scenarios.
Answer: Two examples from the book:
- Voice Assistants — They process speech and give responses
Example: Google Assistant, Siri, Alexa - Language Translation — Converts text or speech from one language to another
Example: Google Translate
Both use NLP to understand and process natural language.
Que 7. Explain the process of stemming and lemmatization in text processing, supported by an example.
Answer: Stemming
- Removes affixes and converts words to root form
- The resulting word may not be meaningful
- Example from book:
studies → studi (not meaningful)
Lemmatization
- Also removes affixes
- But ensures the output (lemma) is a meaningful word
- Example:
studies → study
Que 8. Describe any four applications of TFIDF.
Answer: Four applications are:
- Document Classification
- Topic Modelling
- Information Retrieval System
- Stop Word Filtering
Que 9. Samiksha, a student of class X was exploring the Natural Language Processing domain. She got stuck while performing the text normalisation. Help her to normalise the text on the segmented sentences given below:
Document 1: Akash and Ajay are best friends.
Document 2: Akash likes to play football but Ajay prefers to play online games.
Answer: Following are the Text Normalisation steps:
- Step 1 — Sentence Segmentation
- Already single-sentence documents.
- Step 2 — Tokenisation
- Document 1 → [akash, and, ajay, are, best, friends]
- Document 2 → [akash, likes, to, play, football, but, ajay, prefers, to, play, online, games]
- Step 3 — Remove stop-words, numbers & special symbols
- Stop-words include words like and, are, to, but etc.
- Document 1 → [akash, ajay, best, friends]
- Document 2 → [akash, likes, play, football, ajay, prefers, play, online, games]
- Step 4 — Convert to lowercase
- Already lowercase.
- Step 5 — Stemming / Lemmatization (optional)
- friends → friend
- games → game
- ✔ Final Normalised Output
- Document 1 → [akash, ajay, best, friend]
- Document 2 → [akash, like, play, football, ajay, prefer, play, online, game]
Que 10. Through a step-by-step process, calculate TFIDF for the given corpus
Document 1: Johny Johny Yes Papa,
Document 2: Eating sugar? No Papa
Document 3: Telling lies? No Papa
Document 4: Open your mouth, Ha! Ha! Ha!
Answer: Corpus
- Johny Johny Yes Papa
- Eating sugar? No Papa
- Telling lies? No Papa
- Open your mouth Ha Ha Ha
We follow the steps TF → DF → IDF → TFIDF:
Step 1 — Create Vocabulary (unique words)
johny, yes, papa, eating, sugar, no, telling, lies, open, your, mouth, ha
Step 2 — Term Frequency (TF)
| Word | D1 | D2 | D3 | D4 |
| johny | 2 | 0 | 0 | 0 |
| yes | 1 | 0 | 0 | 0 |
| papa | 1 | 1 | 1 | 0 |
| eating | 0 | 1 | 0 | 0 |
| sugar | 0 | 1 | 0 | 0 |
| no | 0 | 1 | 1 | 0 |
| telling | 0 | 0 | 1 | 0 |
| lies | 0 | 0 | 1 | 0 |
| open | 0 | 0 | 0 | 1 |
| your | 0 | 0 | 0 | 1 |
| mouth | 0 | 0 | 0 | 1 |
| ha | 0 | 0 | 0 | 3 |
Step 3 — Document Frequency (DF)
| Word | DF |
| papa | 3 |
| no | 2 |
| johny, yes, eating, sugar, telling, lies, open, your, mouth, ha | 1 |
Step 4 — Inverse Document Frequency (IDF)
Total documents = 4
IDF = 4 / DF(word)
Step 5 — TF × IDF (TF-IDF Concept Meaning)
- Words appearing in fewer documents get higher value
- Words like papa occur in many documents → lower value
- Rare words like ha, open, sugar → higher value
By Anjeev Kr Singh – Computer Science Educator
Published on : January 6, 2026 | Updated on : January 6, 2026
About the Author
