MODELCATALOG

Build the vocabulary needed to understand every later NLP architecture.

NLP / Foundations / Basic

1. NLP Mental Model: What Problem Are We Solving?

Build the vocabulary needed to understand every later NLP architecture.

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

    Distinguish structured, image, sequential, and language data.

  2. Objective 2

    Understand why word order and context matter.

  3. Objective 3

    Map common NLP use cases to model families.

  4. Objective 4

    Separate representation (features) from prediction (model).

02 Core idea

NLP turns human language into numerical representations that algorithms can process. A complete NLP system usually has four layers:

  1. Raw language — sentences, documents, chats, transcripts.
  2. Representation — counts, TF-IDF, embeddings, token IDs, hidden states.
  3. Model — logistic regression, ANN, RNN/LSTM, Transformer, LLM.
  4. Task/output — class, entity, score, translated text, answer, summary.

The most important early distinction is non-sequential vs sequential information. In tabular prediction, the feature *position* identifies the feature. In language, the *order of tokens* itself carries meaning.

03 ANN vs RNN intuition

A feed-forward ANN receives a fixed feature vector and does not have an explicit recurrent memory. An RNN processes a sequence one timestep at a time and carries a hidden state from the previous timestep.

Example:

The food is good

The food is not good

A bag-of-words representation may know which words are present but can weaken the importance of ordering. A recurrent model can update its state as it sees The → food → is → not → good.

04 Use-case map

  • Classification: complaint category, sentiment, intent, spam.
  • Sequence labeling: named-entity recognition, POS tagging.
  • Similarity/search: semantic retrieval, duplicate detection.
  • Generation: completion, summarization, drafting.
  • Sequence-to-sequence: translation.
  • Question answering: extractive or generative QA.
  • Retrieval-Augmented Generation: retrieve evidence, then generate an answer.

05 Pitfalls and nuances

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

  1. Pitfall 1

    Thinking 'NLP model' and 'text representation' are the same thing.

  2. Pitfall 2

    Assuming deep learning is automatically better than TF-IDF + linear models.

  3. Pitfall 3

    Ignoring whether the task actually requires word order or long-range context.

06 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

    Can you explain why `not good` is a sequence problem?

  2. Check 2

    Can you name one NLP problem where TF-IDF may be sufficient?

  3. Check 3

    Can you separate feature extraction from classification?