Document processing pipeline in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a document processing pipeline in NLP?

A document processing pipeline is a series of steps that take raw text documents and transform them into useful information by cleaning, analyzing, and extracting data.

beginner

Name three common steps in a document processing pipeline.

Common steps include: 1) Text cleaning (removing noise), 2) Tokenization (splitting text into words), 3) Feature extraction (turning words into numbers).

beginner

Why is tokenization important in document processing?

Tokenization breaks text into smaller pieces like words or sentences, making it easier for computers to analyze and understand the text.

intermediate

What role does feature extraction play in a document processing pipeline?

Feature extraction converts text into numerical data that machine learning models can use to learn patterns and make predictions.

intermediate

How can a document processing pipeline handle different document formats like PDFs or images?

It uses specialized tools to convert PDFs or images into text first, such as OCR (Optical Character Recognition), before applying NLP steps.

Which step in a document processing pipeline splits text into words?

A Tokenization

B Feature extraction

C Text cleaning

D Model training

Tokenization is the process of splitting text into smaller units like words.

What is the main purpose of text cleaning in a document pipeline?

A To train the machine learning model

B To remove unwanted characters and noise

C To convert text into numbers

D To split text into sentences

Text cleaning removes noise like punctuation or extra spaces to prepare text for analysis.

Which tool is commonly used to extract text from images in document processing?

A Tokenizer

B Stopword remover

C OCR

D Stemmer

OCR (Optical Character Recognition) extracts text from images or scanned documents.

Feature extraction in NLP pipelines converts text into what?

A Raw text

B Images

C Audio signals

D Numerical data

Feature extraction turns text into numbers so models can process it.

What is the correct order of these pipeline steps: Tokenization, Text cleaning, Feature extraction?

A Text cleaning → Tokenization → Feature extraction

B Feature extraction → Tokenization → Text cleaning

C Tokenization → Text cleaning → Feature extraction

D Feature extraction → Text cleaning → Tokenization

First clean text, then split into tokens, then extract features.

Describe the main steps of a document processing pipeline and why each is important.

Think about how raw text becomes useful data for models.

Explain how a document processing pipeline can handle different types of documents like scanned images or PDFs.

Focus on converting non-text formats into text first.