Chaining chunk transformations
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
The transform functions defined in the previous recipes can be chained together to normalize chunks. The resulting chunks are often shorter with no loss of meaning.
In transforms.py is the function transform_chunk(). It takes a single chunk and an optional list of transform functions. It calls each transform function on the chunk, one at a time, and returns the final chunk:
def transform_chunk(chunk, chain=[filter_insignificant, swap_verb_phrase, swap_infinitive_phrase, singularize_plural_noun], trace=0):
for f in chain:
chunk = f(chunk)
if trace:
print f.__name__, ':', chunk
return chunkUsing it on the phrase the book of recipes is delicious, we get delicious recipe book:
>>> from transforms import transform_chunk
>>> transform_chunk([('the', 'DT'), ('book', 'NN'), ('of', 'IN'), ('recipes', 'NNS'), ('is', 'VBZ'), ('delicious', 'JJ')])
[('delicious', 'JJ'), ('recipe', 'NN'), ('book', 'NN')]The transform_chunk() function defaults to chaining the following functions in the given order:
filter_insignificant()swap_verb_phrase()swap_infinitive_phrase()singularize_plural_noun()
Each function transforms the chunk that results from the previous function, starting with the original chunk.
You can pass trace=1 into transform_chunk() to get an output at each step:
>>> from transforms import transform_chunk
>>> transform_chunk([('the', 'DT'), ('book', 'NN'), ('of', 'IN'), ('recipes', 'NNS'), ('is', 'VBZ'), ('delicious', 'JJ')], trace=1)
filter_insignificant : [('book', 'NN'), ('of', 'IN'), ('recipes', 'NNS'), ('is', 'VBZ'), ('delicious', 'JJ')]
swap_verb_phrase : [('delicious', 'JJ'), ('book', 'NN'), ('of', 'IN'), ('recipes', 'NNS')]
swap_infinitive_phrase : [('delicious', 'JJ'), ('recipes', 'NNS'), ('book', 'NN')]
singularize_plural_noun : [('delicious', 'JJ'), ('recipe', 'NN'), ('book', 'NN')]
[('delicious', 'JJ'), ('recipe', 'NN'), ('book', 'NN')]This shows you the result of each transform function, which is then passed in to the next transform until a final chunk is returned.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.