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.

Practice

1. What is the main purpose of a document processing pipeline in NLP?

easy

A. To break down text tasks into smaller, manageable steps

B. To store documents in a database

C. To translate documents into multiple languages

D. To generate random text from documents

Solution

1. Step 1: Understand the pipeline concept

   A document processing pipeline divides a big task into smaller steps to handle text better.

2. Step 2: Identify the main goal

   The goal is to make complex text easier to process by breaking it down.

3. Final Answer:

   To break down text tasks into smaller, manageable steps -> Option A

4. Quick Check:

   Pipeline purpose = break down tasks [OK]

Hint: Think of a pipeline as a step-by-step recipe for text [OK]

Common Mistakes:

• Confusing pipeline with storage or translation
• Thinking pipeline generates text
• Ignoring the step-by-step nature

2. Which of the following is the correct order of steps in a simple document processing pipeline?

easy

A. Stopword Removal -> Lemmatization -> Tokenization

B. Lemmatization -> Tokenization -> Stopword Removal

C. Tokenization -> Stopword Removal -> Lemmatization

D. Tokenization -> Lemmatization -> Stopword Removal

Solution

1. Step 1: Recall common pipeline steps

   Tokenization splits text into words, stopword removal deletes common words, lemmatization reduces words to base form.

2. Step 2: Determine logical order

   First split text (tokenize), then remove stopwords, then lemmatize remaining words.

3. Final Answer:

   Tokenization -> Stopword Removal -> Lemmatization -> Option C

4. Quick Check:

   Order = tokenize, remove stopwords, lemmatize [OK]

Hint: Split text first, then clean, then normalize words [OK]

Common Mistakes:

• Removing stopwords before tokenizing
• Lemmatizing before tokenizing
• Mixing step order randomly

3. Given this Python snippet in a document pipeline:

text = "Cats are running fast"
tokens = text.lower().split()
filtered = [w for w in tokens if w not in ['are', 'is', 'the']]
print(filtered)

What is the output?

medium

A. ['cats', 'running', 'fast']

B. ['Cats', 'are', 'running', 'fast']

C. ['cats', 'are', 'running', 'fast']

D. ['running', 'fast']

Solution

1. Step 1: Lowercase and split text

   "Cats are running fast" becomes ['cats', 'are', 'running', 'fast'] after lower() and split().

2. Step 2: Remove stopwords

   Words 'are', 'is', 'the' are removed, so 'are' is removed from the list.

3. Final Answer:

   ['cats', 'running', 'fast'] -> Option A

4. Quick Check:

   Stopwords removed = ['cats', 'running', 'fast'] [OK]

Hint: Lowercase then remove stopwords from tokens [OK]

Common Mistakes:

• Not lowercasing before filtering
• Including stopwords in output
• Confusing original and filtered lists

4. This code is part of a document pipeline:

def clean_text(text):
    tokens = text.split()
    tokens = [t.lower() for t in tokens]
    tokens = [t for t in tokens if t not in stopwords]
    tokens = lemmatize(tokens)
    return tokens

stopwords = ['and', 'the', 'is']

print(clean_text("The cats and dogs are playing"))

What is the error here?

medium

A. text.split() should be text.lower().split()

B. lemmatize function is not defined

C. stopwords list is empty

D. tokens list is not returned

Solution

1. Step 1: Check function definitions

   The code calls lemmatize(tokens) but no lemmatize function is defined or imported.

2. Step 2: Verify other parts

   stopwords list is defined, tokens are returned, and text is split correctly.

3. Final Answer:

   lemmatize function is not defined -> Option B

4. Quick Check:

   Missing lemmatize function causes error [OK]

Hint: Check if all functions used are defined or imported [OK]

Common Mistakes:

• Assuming lemmatize is built-in
• Ignoring missing function errors
• Thinking stopwords list is empty

5. You want to build a document processing pipeline that extracts keywords from large documents. Which sequence of steps is best?

hard

A. POS Tagging -> Keyword Extraction -> Tokenization -> Stopword Removal

B. Keyword Extraction -> Tokenization -> Stopword Removal -> POS Tagging

C. Stopword Removal -> Tokenization -> Keyword Extraction -> POS Tagging

D. Tokenization -> Stopword Removal -> POS Tagging -> Keyword Extraction

Solution

1. Step 1: Understand keyword extraction needs

   Extracting keywords requires clean tokens and knowing word types (POS tags) to pick important words.

2. Step 2: Arrange logical steps

   First tokenize text, remove stopwords to clean, then tag parts of speech, finally extract keywords based on tags.

3. Final Answer:

   Tokenization -> Stopword Removal -> POS Tagging -> Keyword Extraction -> Option D

4. Quick Check:

   Pipeline order = tokenize, clean, tag, extract [OK]

Hint: Clean tokens before tagging and extracting keywords [OK]

Common Mistakes:

• Extracting keywords before tokenizing
• Tagging before cleaning tokens
• Wrong step order breaks pipeline