
- Python - Text Processing
- Python - Text Processing Introduction
- Python - Text Processing Environment
- Python - String Immutability
- Python - Sorting Lines
- Python - Reformatting Paragraphs
- Python - Counting Token in Paragraphs
- Python - Binary ASCII Conversion
- Python - Strings as Files
- Python - Backward File Reading
- Python - Filter Duplicate Words
- Python - Extract Emails from Text
- Python - Extract URL from Text
- Python - Pretty Print
- Python - Text Processing State Machine
- Python - Capitalize and Translate
- Python - Tokenization
- Python - Remove Stopwords
- Python - Synonyms and Antonyms
- Python - Text Translation
- Python - Word Replacement
- Python - Spelling Check
- Python - WordNet Interface
- Python - Corpora Access
- Python - Tagging Words
- Python - Chunks and Chinks
- Python - Chunk Classification
- Python - Text Classification
- Python - Bigrams
- Python - Process PDF
- Python - Process Word Document
- Python - Reading RSS feed
- Python - Sentiment Analysis
- Python - Search and Match
- Python - Text Munging
- Python - Text wrapping
- Python - Frequency Distribution
- Python - Text Summarization
- Python - Stemming Algorithms
- Python - Constrained Search
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Python - Corpora Access
Corpora is a group presenting multiple collections of text documents. A single collection is called corpus. One such famous corpus is the Gutenberg Corpus which contains some 25,000 free electronic books, hosted at http://www.gutenberg.org/. In the below example we access the names of only those files from the corpus which are plain text with filename ending as .txt.
from nltk.corpus import gutenberg fields = gutenberg.fileids() print(fields)
When we run the above program, we get the following output −
[austen-emma.txt', austen-persuasion.txt', austen-sense.txt', bible-kjv.txt', blake-poems.txt', bryant-stories.txt', burgess-busterbrown.txt', carroll-alice.txt', chesterton-ball.txt', chesterton-brown.txt', chesterton-thursday.txt', edgeworth-parents.txt', melville-moby_dick.txt', milton-paradise.txt', shakespeare-caesar.txt', shakespeare-hamlet.txt', shakespeare-macbeth.txt', whitman-leaves.txt']
Accessing Raw Text
We can access the raw text from these files using sent_tokenize function which is also available in nltk. In the below example we retrieve the first two paragraphs of the blake poen text.
from nltk.tokenize import sent_tokenize from nltk.corpus import gutenberg sample = gutenberg.raw("blake-poems.txt") token = sent_tokenize(sample) for para in range(2): print(token[para])
When we run the above program we get the following output −
[Poems by William Blake 1789] SONGS OF INNOCENCE AND OF EXPERIENCE and THE BOOK of THEL SONGS OF INNOCENCE INTRODUCTION Piping down the valleys wild, Piping songs of pleasant glee, On a cloud I saw a child, And he laughing said to me: "Pipe a song about a Lamb!" So I piped with merry cheer.
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