Converting HTML entities with BeautifulSoup
by Iti Mathur, Nisheeth Joshi, Deepti Chopra, Jacob Perkins, Nitin Hardeniya
Natural Language Processing: Python and NLTK
- Natural Language Processing: Python and NLTK
- Table of Contents
- Natural Language Processing: Python and NLTK
- Natural Language Processing: Python and NLTK
- Credits
- Preface
- 1. Module 1
- 1. Introduction to Natural Language Processing
- 2. Text Wrangling and Cleansing
- 3. Part of Speech Tagging
- 4. Parsing Structure in Text
- 5. NLP Applications
- Building your first NLP application
- Other NLP applications
- Summary
- 6. Text Classification
- 7. Web Crawling
- 8. Using NLTK with Other Python Libraries
- 9. Social Media Mining in Python
- 10. Text Mining at Scale
- 2. Module 2
- 1. Tokenizing Text and WordNet Basics
- Introduction
- Tokenizing text into sentences
- Tokenizing sentences into words
- Tokenizing sentences using regular expressions
- Training a sentence tokenizer
- Filtering stopwords in a tokenized sentence
- Looking up Synsets for a word in WordNet
- Looking up lemmas and synonyms in WordNet
- Calculating WordNet Synset similarity
- Discovering word collocations
- 2. Replacing and Correcting Words
- 3. Creating Custom Corpora
- Introduction
- Setting up a custom corpus
- Creating a wordlist corpus
- Creating a part-of-speech tagged word corpus
- Creating a chunked phrase corpus
- Creating a categorized text corpus
- Creating a categorized chunk corpus reader
- Lazy corpus loading
- Creating a custom corpus view
- Creating a MongoDB-backed corpus reader
- Corpus editing with file locking
- 4. Part-of-speech Tagging
- Introduction
- Default tagging
- Training a unigram part-of-speech tagger
- Combining taggers with backoff tagging
- Training and combining ngram taggers
- Creating a model of likely word tags
- Tagging with regular expressions
- Affix tagging
- Training a Brill tagger
- Training the TnT tagger
- Using WordNet for tagging
- Tagging proper names
- Classifier-based tagging
- Training a tagger with NLTK-Trainer
- 5. Extracting Chunks
- Introduction
- Chunking and chinking with regular expressions
- Merging and splitting chunks with regular expressions
- Expanding and removing chunks with regular expressions
- Partial parsing with regular expressions
- Training a tagger-based chunker
- Classification-based chunking
- Extracting named entities
- Extracting proper noun chunks
- Extracting location chunks
- Training a named entity chunker
- Training a chunker with NLTK-Trainer
- 6. Transforming Chunks and Trees
- Introduction
- Filtering insignificant words from a sentence
- Correcting verb forms
- Swapping verb phrases
- Swapping noun cardinals
- Swapping infinitive phrases
- Singularizing plural nouns
- Chaining chunk transformations
- Converting a chunk tree to text
- Flattening a deep tree
- Creating a shallow tree
- Converting tree labels
- 7. Text Classification
- Introduction
- Bag of words feature extraction
- Training a Naive Bayes classifier
- Training a decision tree classifier
- Training a maximum entropy classifier
- Training scikit-learn classifiers
- Measuring precision and recall of a classifier
- Calculating high information words
- Combining classifiers with voting
- Classifying with multiple binary classifiers
- Training a classifier with NLTK-Trainer
- 8. Distributed Processing and Handling Large Datasets
- Introduction
- Distributed tagging with execnet
- Distributed chunking with execnet
- Parallel list processing with execnet
- Storing a frequency distribution in Redis
- Storing a conditional frequency distribution in Redis
- Storing an ordered dictionary in Redis
- Distributed word scoring with Redis and execnet
- 9. Parsing Specific Data Types
- A. Penn Treebank Part-of-speech Tags
- 1. Tokenizing Text and WordNet Basics
- 3. Module 3
- 1. Working with Strings
- 2. Statistical Language Modeling
- Understanding word frequency
- Applying smoothing on the MLE model
- Develop a back-off mechanism for MLE
- Applying interpolation on data to get mix and match
- Evaluate a language model through perplexity
- Applying metropolis hastings in modeling languages
- Applying Gibbs sampling in language processing
- Summary
- 3. Morphology – Getting Our Feet Wet
- 4. Parts-of-Speech Tagging – Identifying Words
- 5. Parsing – Analyzing Training Data
- 6. Semantic Analysis – Meaning Matters
- 7. Sentiment Analysis – I Am Happy
- 8. Information Retrieval – Accessing Information
- 9. Discourse Analysis – Knowing Is Believing
- 10. Evaluation of NLP Systems – Analyzing Performance
- B. Bibliography
- Index
HTML entities are strings such as "&" or "<". These are encodings of normal ASCII characters that have special uses in HTML. For example, "<" is the entity for "<", but you can't just have "<" within HTML tags because it is the beginning character for an HTML tag, hence the need to escape it and define the "<" entity. "&" is the entity code for "&", which as we've just seen is the beginning character for an entity code. If you need to process the text within an HTML document, then you'll want to convert these entities back to their normal characters so you can recognize them and handle them appropriately.
You'll need to install BeautifulSoup, which you should be able to do with sudo pip install beautifulsoup4 or sudo easy_install beautifulsoup4. You can read more about BeautifulSoup at http://www.crummy.com/software/BeautifulSoup/.
BeautifulSoup is an HTML parser library that can also be used for entity conversion. It's quite simple: create an instance of BeautifulSoup given a string containing HTML entities, then get the string attribute:
>>> from bs4 import BeautifulSoup
>>> BeautifulSoup('<').string
'<'
>>> BeautifulSoup('&').string
'&'However, the reverse is not true. If you try to do BeautifulSoup('<'), you will get a None result because that is not valid in HTML.
To convert the HTML entities, BeautifulSoup looks for tokens that look like an entity and replaces them with their corresponding value in the htmlentitydefs.name2codepoint dictionary from the Python standard library. It can do this if the entity token is within an HTML tag, or when it's in a normal string.
BeautifulSoup is an excellent HTML and XML parser in its own right, and can be a great alternative to lxml. It's particularly good at handling malformed HTML. You can read more about how to use it at http://www.crummy.com/software/BeautifulSoup/bs4/doc/.
Here's an example of using BeautifulSoup to extract URLs, like we did in the Extracting URLs from HTML with lxml recipe. You first create the soup with an HTML string, call the findAll() method with 'a' to get all anchor tags, and pull out the 'href' attribute to get the URLs:
>>> from bs4 import BeautifulSoup
>>> soup = BeautifulSoup('Hello <a href="/world">world</a>')
>>> [a['href'] for a in soup.findAll('a')]
['/world']-
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