COURSE 15Intermediate11 IN-DEPTH LESSONS

NLP Beyond Chatbots

Text understanding beyond chat: Named Entity Recognition (NER), machine translation, document-level information extraction, and summarization evaluation.

Start Lesson 1: NLP beyond chatbots →← All 29 Courses

Course Syllabus & Units

Unit 1

Overview

1 lessons
1.1L1

NLP beyond chatbots

**Natural language processing (NLP)** is the broader toolbox for working with language: search, classification, translation, information extraction, summarization, parsing, and more—not only chat UIs. Stanford CS224N introduces deep lear...

Read lesson →
Unit 2

Extraction

1 lessons
2.1L1

Named entity recognition

**Named entity recognition (NER)** finds and labels entities in text—people, organizations, places, dates, products, and domain types you define—so systems can structure messy language. SLP3 places sequence labeling for parts of speech a...

Read lesson →
Unit 3

Mt

1 lessons
3.1L1

Machine translation basics

**Machine translation (MT)** maps text from one language to another. Modern MT is largely neural encoder–decoder / Transformer seq2seq. SLP3 includes a dedicated MT chapter; BART-style denoising pretraining also transfers to translation ...

Read lesson →
Unit 4

Classify

1 lessons
4.1L1

Text classification practice

**Text classification** assigns labels to documents or messages: topic, intent, sentiment, priority, language. SLP3 opens volume I with logistic regression and text classification before climbing into neural nets and LLMs—because classif...

Read lesson →
Unit 5

Summarize

1 lessons
5.1L1

Summarization evaluation

**Summarization** shortens text; **summarization evaluation** judges whether the short version is faithful, complete enough, and useful—not only fluent. BART (Lewis et al., 2020) is a seq2seq pretrained model that became a strong abstrac...

Read lesson →
Unit 6

Ir

1 lessons
6.1L1

Classic information retrieval

**Information retrieval (IR)** ranks documents for a query. Classic IR uses inverted indexes and lexical scoring (TF-IDF, BM25). SLP3 includes an **Information Retrieval and Retrieval-Augmented Generation** chapter—signal that keyword se...

Read lesson →
Unit 7

Rules

1 lessons
7.1L1

Regex and rules still matter

Deterministic **rules and regular expressions** catch crisp structure—IDs, formats, allowlists, required legal phrases—either instead of ML or as **gates** around ML/LLM systems. SLP3’s early chapters on words/tokens and classic classifi...

Read lesson →
Unit 8

Bert

1 lessons
8.1L1

Masked LMs and BERT

**BERT** (Bidirectional Encoder Representations from Transformers) pretrains a Transformer **encoder** on unlabeled text with **masked language modeling** and next-sentence-style objectives, then fine-tunes with a small task head for cla...

Read lesson →
Unit 9

Seq2seq

1 lessons
9.1L1

BART and seq2seq pretraining

**BART** is a **denoising autoencoder** for pretraining sequence-to-sequence Transformers: corrupt text with a noising function, then train the model to reconstruct the original. Lewis et al. (ACL 2020) describe it as generalizing ideas ...

Read lesson →
Unit 10

Ie

1 lessons
10.1L1

Document-level information extraction

**Document-level information extraction (doc-IE)** pulls structured facts—entities, relations, events—from **whole documents**, not only single sentences. Zheng, Wang, and Huang (FuturED 2024) survey contemporary doc-IE, analyze errors o...

Read lesson →
Unit 11

Map

1 lessons
11.1L1

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...

Read lesson →