Natural Language Processing · TEE · papers
Q2b · NLP applications and limitations
2024-02-12 · re-exam · recovered AI paper
Previous examination · complete worked answers
Answer
Natural language processing (NLP) is the field concerned with computational methods for analysing, interpreting and generating human language. Natural languages such as English, Hindi and Marathi evolved through human use; their meanings are not specified by the rigid rules of a programming language.
An NLP system may receive text, speech converted to text, or another linguistic representation. Its output depends on the task: a category, a tag for every word, a translated sentence, a ranked document list or a generated answer. It does not always “understand” in the human sense; its performance must be evaluated for the specific task.
| Task | Input | Output |
|---|---|---|
| Sentiment analysis | “The service was excellent.” | Positive sentiment |
| Text classification | An email | Spam / not spam |
| POS tagging | “Birds fly.” | Birds/NOUN fly/VERB |
| Named entity recognition | “Asha joined Google in Mumbai.” | Asha: person; Google: organisation; Mumbai: location |
| Machine translation | A Hindi sentence | A corresponding English sentence |
| Information retrieval | A search query | Ranked relevant documents |
| Question answering | Question plus knowledge/context | An answer |
| Summarization | A long article | A shorter text preserving key information |
How NLP relates to AI, ML and DL
AI is the broader study of systems performing tasks associated with intelligent behaviour. Machine learning learns patterns from data. Deep learning uses multiple trainable neural layers. NLP is an application/research field that can use hand-written rules, statistical ML or deep learning. Therefore, not every NLP system is a neural network.
Linguistics supplies knowledge about language structure. Statistics supplies uncertainty models. Computer science supplies algorithms, storage and efficient implementation. A practical NLP system combines these ideas.
NLP = human language ko computational task mein badlo: label, meaning, search ya generated sentence.
Ambiguity means an expression supports more than one interpretation. Good exam answers name the ambiguity and explain both readings.
| Challenge | Example | Why a system can fail |
|---|---|---|
| Lexical/sense ambiguity | bank: finance or river edge | One written word has several senses |
| Syntactic ambiguity | I saw the man with a telescope. | The telescope may modify saw or the man |
| Reference ambiguity | Riya told Neha that she won. | She may refer to either person without more context |
| Sarcasm | Great, another delay. | Literal positive word conveys frustration |
| Negation | The film was not good. | Dropping not reverses meaning |
| Domain shift | “Positive” in a medical report | Meaning/use differs by domain |
| Code mixing | Kal meeting reschedule karna. | Several languages and spelling conventions |
| Noisy text | tmrw, repeated letters, typos | Vocabulary/token boundaries become uncertain |
| Low-resource setting | Few labelled examples in a language | Models lack representative training evidence |
| Bias and privacy | Skewed data or personal details | Errors and harms can persist in deployed systems |
Homophones sound alike but may differ in spelling (pair/pear). Homographs share spelling but can differ in meaning or pronunciation (lead the team / lead metal). These are different dimensions from syntactic ambiguity.
Long-distance dependencies, cultural knowledge, idioms, morphology and multilingual writing systems also complicate processing. For “kick the bucket”, literal word-by-word interpretation can miss the idiomatic meaning. For “The book that the students recommended is useful”, the verb is agrees with book across an intervening phrase.
Exam mein challenge ka naam + ek sentence + dono meanings/actual failure likho.