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.