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Verified 2026-08-14

NLP task map beyond chat

A **field map** of NLP tasks you should recognize beyond chatbots—drawn from Stanford CS224N’s deep-learning NLP arc and the Jurafsky & Martin SLP3 table of contents (Jan 6, 2026 draft release noted on the book site). Use it to place any...

What it is

A field map of NLP tasks you should recognize beyond chatbots—drawn from Stanford CS224N’s deep-learning NLP arc and the Jurafsky & Martin SLP3 table of contents (Jan 6, 2026 draft release noted on the book site). Use it to place any product problem on the right shelf.

<!-- IMAGE: two-column map — Volume I LLMs vs Volume II linguistic structure -->

HIGH PRIORITYDIAGRAM
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

Full NLP task map poster (detailed): columns Token/Sequence labeling | Classification | Generation | Retrieval | Structured IE | Multilingual. Rows list tasks (POS, NER, sentiment, NLI, MT, summarization, QA, IR, relation extraction, coref). ChatLLM sits as one cell with note 'generalist interface'. Color by input/output type.

Educational Focus: Course-level navigation aid—highest leverage visual for Course 15.

Why it matters

Without a map, every problem becomes “prompt a chatbot.” With a map, you pick classification vs IR vs IE vs MT vs speech, and you know which textbook chapter or course assignment builds the skill.

How it works (plain)

From SLP3 Volume I (LLMs & foundations): words/tokens, n-grams, classification, embeddings, neural nets, LLMs, Transformers, post-training/alignment, masked LMs, IR/RAG, MT, RNNs (historical), plus speech chapters (phonetics, ASR, TTS).

From SLP3 Volume II (structure): POS/NER sequence labeling, constituency & dependency parsing, IE (relations/events/time), semantic roles, sentiment lexicons, coreference, discourse, conversation structure.

From CS224N (Winter 2026 public description): word vectors → neural foundations / dependency parsing → self-attention/Transformers → LLM benchmarking/evaluation → final project (default minimal GPT-style project or custom). Public lecture videos exist for prior offerings; slides/assignments update yearly.

Everyday example

“Summarize tickets and route them” is at least two map cells: classification (route) + summarization (compress)—plus IR if you ground in past tickets.

Try it

Circle five SLP3 chapter titles that match your work. Star one CS224N-style skill to practice next (e.g., implement a tiny Transformer attention block—conceptually, in Course 07 labs).

Myths

⚠️ Myth: ChatGPT-era NLP = one task.
✓ Reality: The TOC is still wide—chat is conversation + generation + tools.
⚠️ Myth: Linguistic structure chapters are obsolete.
✓ Reality: They name failure modes (coreference, discourse) that long-context models still hit (see doc-IE survey).
⚠️ Myth: You must complete every chapter before shipping.
✓ Reality: Maps prioritize—depth follows your product risks.

Sources