MODELCATALOG

Connect architectures to real business outcomes.

NLP / Applications / Intermediate → Advanced

12. Core NLP Task Patterns

Connect architectures to real business outcomes.

01 Foundations Represent language 02 Classical NLP Build baselines 03 Neural NLP Learn sequence 04 Transformers Learn attention 05 Applications Apply models 06 Engineering Operate safely 07 Capstone Integrate

01 Learning objectives

By the end of this module, you should be able to explain the concept in plain language, connect it to a use case, and recognize when it is — and is not — appropriate.

  1. Objective 1

    Recognize task/output shapes.

  2. Objective 2

    Choose metrics appropriate to the task.

  3. Objective 3

    Understand model-family choices.

02 Classification

Input: document/sentence

Output: one or more labels

Examples: complaint routing, fraud narrative classification, intent detection.

03 Token classification / NER

Input: token sequence

Output: label per token

Examples: customer name, account number, organization, date, location.

04 Semantic similarity

Input: two texts or query + document corpus

Output: similarity score / ranked results

Examples: duplicate complaints, knowledge retrieval.

05 Generation

Input: prompt/context

Output: generated token sequence

Examples: summaries, explanations, draft responses.

06 Pitfalls and nuances

These are the mistakes most likely to create a wrong mental model or a weak implementation.

  1. Pitfall 1

    Using the same metric for every NLP task.

  2. Pitfall 2

    Ignoring calibration when probabilities trigger automated business decisions.

07 Checkpoint questions

Try answering these without looking back at the module. If the explanation is clear in your own words, you are ready to continue.

  1. Check 1

    Which task produces one label per token?

  2. Check 2

    Which task powers vector-based retrieval?