Applications of NLP
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Applications of NLP
Recall first
For each task—translation, search, sentiment analysis, and question answering—what is the input, output, and main failure risk? Try before reading.
First principles
An NLP application is a task definition plus language representations, inference, and an evaluation protocol. The same text may be processed differently depending on the output required:
- Machine translation: source text → target text. It must preserve meaning while adapting grammar and style; adequacy and fluency can conflict.
- Text summarization: document(s) → shorter text. Extractive systems select source spans; abstractive systems generate, so factuality and coverage must be checked.
- Sentiment/opinion analysis: text → polarity, emotion, or aspect-level opinion. Sarcasm, negation, and domain-specific meaning are hard.
- Information retrieval: query + collection → ranked documents/passages. Relevance ranking and efficient indexing matter; a fluent answer is not evidence of retrieval.
- Question answering: question + evidence or knowledge base → answer. Retrieval, reading/comprehension, grounding, and answerability are separate concerns.
- Information extraction: text → structured entities, relations, events, or attributes.
- Dialogue and assistants: turns + context → response/action. State, safety, and intent ambiguity make errors consequential.
- Spell/grammar checking and tutoring: detect or explain likely errors; helpfulness requires calibrated suggestions, not blind correction.
Worked design. For “Is the new phone worth buying?” a pipeline might retrieve reviews, classify aspect sentiment (battery, price), aggregate evidence, and generate a cited answer. Evaluation must separately test retrieval recall, sentiment labels, factual aggregation, and generation faithfulness. “Good English” alone is not task success.
Applications differ in whether false positives or false negatives are worse. A medical triage system needs recall and safe uncertainty handling; a search engine may optimize ranking metrics; a translation system needs human or task-based evaluation. Always state the target users, languages, domain, and cost of error.
Exercise — reveal after answering
Why is ROUGE-style overlap insufficient by itself for evaluating an abstractive summary?
Answer: A summary can use different valid wording, while an overlapping summary can contain unsupported or wrong claims. Evaluate factuality, relevance, coverage, and readability alongside overlap.
Exam lens
For any application write: input → NLP subproblems → output → metric → limitation. Distinguish retrieval (find evidence) from generation (write text) and classification (choose labels).
Rapid revision checklist
- Define translation, summarization, sentiment, IR, and QA.
- State one failure mode per application.
- Match evaluation to task and error cost.
- Separate evidence retrieval from answer generation.
Key takeaways
- NLP applications are not interchangeable: output, evidence, and errors differ.
- A multi-stage system needs component and end-to-end evaluation.
- User, language, domain, and safety constraints determine what “good” means.