Simulated Example for NLP IA1 (1)
Internal Assessment 1 - Solutions for Natural Language Processing
Date: 17/02/26
| Q. No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Total Marks |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mark Awarded | 10 | 09 | 19 |
| Q1 | Solve any Two |
|---|---|
| i. | Define NLP. What are the different ambiguities in NLP. |
| ii. | Explain N-gram Language model. |
| iii. | Compare inflectional morphology and derivational morphology. |
Natural language processing (NLP) is the part of artificial intelligence where the computer tries to learn the text and speech in human language.
It learns to read, write, and speak so that we can understand them easily.
The main goal of natural language processing is to make communication between machines and humans easy.
Ambiguity in natural language processing refers to the possibility of having more than one meaning for one word.
Different types of ambiguities in Natural language Processing are:
(1) Lexical Ambiguity
Lexical ambiguity refers to when one word has more than one meaning.
For example:
"bat", now here, a bat can be a bird or sports equipment.
(2) Parsing Ambiguity
Parsing ambiguity in NLP refers to when one sentence can be interpreted in more than one grammatical way.
For example:
"I saw a girl with a telescope"
Here, it can mean they saw a girl holding a telescope or they saw a girl using a telescope.
(3) Semantic Ambiguity
Semantic ambiguity in NLP refers to when the structure of the sentence is correct, but the meaning is still unclear.
For example: "I saw a girl with a telescope"
The sentence is structurally correct, but the meaning is still unclear.
(4) Pragmatic Ambiguity
Pragmatic ambiguity refers to when the user's intentions are clear, but it depends on the situation and the context. Without context, NLP cannot process further.
NLP needs proper context and needs to overcome all the ambiguities to give a proper output.
Morphology is the study of how words are formed and created. The smallest unit of morphology is called a morpheme. Morphology uses prefixes and suffixes to create words and also changes the form of the words.
Example:
- happiness → happy + ness
- walk → walking.
| Inflectional Morphology | Derivational Morphology |
|---|---|
| • Changes the form of the word (e.g., tenses and numbers). | • Adds prefix or suffix to the word. |
| • Changes the base word into a new word but keeps the meaning of both words the same. | • Forms a new word with a new meaning or grammatical category, though often sharing the same base. |
| • Changes the form of the word. | • Changes the grammatical category or core meaning. |
| • Example: walk (base) → walks, walked, walking | • Example: Happy (base) → unhappy (prefix), happiness (suffix) |
| Q2 | Solve any Two |
|---|---|
| i. | Explain the various challenges in NLP. |
| ii. | Differentiate between Stemming and Lemmatization. |
| iii. | What are Regular Expression (RE)? Explain the meaning of the given RE: • a. [e^] • b. [^A-Z] |
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Lexical Ambiguity: A challenge when one word can have more than one meaning.
Example: "cap" can be either headwear or a pen's lid. -
Parsing Ambiguity: A challenge when one sentence can have more than one grammatical meaning.
Example: "I saw a girl with a telescope" (the sentence has more than one interpretation). -
Semantic Ambiguity: A challenge when the structure is correct but the meaning is still unclear.
-
Pragmatic Ambiguity: A challenge when the context is missing. Without proper context, NLP steps (like the telescope example) cannot yield unique results.
Stemming and lemmatization are methods used to reduce words to their base or root form.
| Stemming | Lemmatization |
|---|---|
| • Reduces word to base form without checking grammar/dictionary. | • Reduces words to base form (lemma) while checking grammar and context. |
| • Output can be grammatically incorrect (e.g., "studi"). | • Output is a real word found in the dictionary. |
| • Faster as it follows simple heuristic rules. | • Slower but more accurate as it uses morphological analysis. |
| • Generally less precise than lemmatization. | • More precise and preferred for complex tasks. |
| • Example: Studying → Studi Studies → Studi | • Example: Studies → Study Mice → Mouse |