Document processing pipeline in NLP

Introduction

A document processing pipeline helps computers understand and organize text documents step-by-step. It breaks down big tasks into smaller, easy steps.

You want to read and summarize many emails automatically.

You need to find important facts from scanned papers.

You want to sort news articles by topic.

You want to check documents for spelling and grammar errors.

You want to translate documents from one language to another.

Syntax

NLP

pipeline = [step1, step2, step3, ...]
for step in pipeline:
    data = step(data)

Each step is a function that changes the document data.

The pipeline runs steps one after another to process the document fully.

Examples

This pipeline splits text into words, then makes all words lowercase.

NLP

def tokenize(text):
    return text.split()

def lowercase(words):
    return [w.lower() for w in words]

pipeline = [tokenize, lowercase]
text = "Hello World"
for step in pipeline:
    text = step(text)
print(text)

This pipeline also removes punctuation from each word.

NLP

def remove_punctuation(words):
    return [w.strip('.,!') for w in words]

pipeline = [tokenize, remove_punctuation, lowercase]
text = "Hello, World!"
for step in pipeline:
    text = step(text)
print(text)

Sample Model

This program processes the text by splitting it into words, removing punctuation, making words lowercase, and counting how many times each word appears.

NLP

def tokenize(text):
    return text.split()

def lowercase(words):
    return [w.lower() for w in words]

def remove_punctuation(words):
    return [w.strip('.,!?') for w in words]

def count_words(words):
    counts = {}
    for w in words:
        counts[w] = counts.get(w, 0) + 1
    return counts

pipeline = [tokenize, remove_punctuation, lowercase, count_words]

text = "Hello, world! Hello world."

result = text
for step in pipeline:
    result = step(result)

print(result)

Important Notes

Each step should take the output of the previous step as input.

You can add or remove steps depending on what you want to do with the document.

Keep steps simple and focused for easier debugging and understanding.

Summary

A document processing pipeline breaks down text tasks into small steps.

Each step changes the data to prepare it for the next step.

This makes handling large or complex documents easier and clearer.

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