4. Parsing Structure in Text
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
This chapter involves a better understanding of deep structure in text and also how to deep parse text and use it in various NLP applications. Now, we are equipped with various NLP preprocessing steps. Let's move to some deeper aspect of the text. The structure of language is so complex that we can describe it by various layers of structural processing. In this chapter we will touch upon all these structures in text, differentiate between them, and provide you with enough details about the usage of one of these. We will talk about context-free grammar (CFG) and how it can be implemented with NLTK. We will also look at the various parsers and how we can use some of the existing parsing methods in NLTK. We will write a shallow parser in NLTK and will again talk about NER in the context of chunking. We will also provide details about some options that exist in NLTK to do deep structural analysis. We will also try to give you some real-world use cases of information extraction and how it can be achieved by using some of the topics that you will learn in this chapter. We want you to have an understanding of these topics by the end of this chapter.
In this chapter:
- We will also see what parsing is and what is the relevance of parsing in NLP.
- We will then explore different parsers and see how we can use NLTK for parsing.
- Finally, we will see how parsing can be used for information extraction.
In deep or full parsing, typically, grammar concepts such as CFG, and probabilistic context-free grammar (PCFG), and a search strategy is used to give a complete syntactic structure to a sentence. Shallow parsing is the task of parsing a limited part of the syntactic information from the given text. While deep parsing is required for more complex NLP applications, such as dialogue systems and summarization, shallow parsing is more suited for information extraction and text mining varieties of applications. I will talk about these in the next few sections with more details about their pros and cons and how we can use them for our NLP application.
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