NLP | Splitting and Merging Chunks

In natural language processing (NLP), text division into pieces that are smaller and easier to handle with subsequent recombination is an essential process. These actions, referred to as splitting and merging, enable systems to comprehend the language structure more effectively and allow for analysis to be conducted more efficiently. Being able to divide text into reasonable segments, like sentences or phrases, is essential in processes such as tokenization, parsing, and named entity recognition. Conversely, combining smaller units is no less crucial for comprehending the general context, as it assists in putting together large meaningful units such as sentences or paragraphs. This article discusses the SplitRule and MergeRule classes in NLP, which ascertain and regulate the rules for dividing text into chunks and reconstructing them into meaningful units, respectively.

SplitRule class

The SplitRule class in NLP is designed to define and manage rules for splitting text into chunks, allowing for fine-grained control over how sentences, words, or phrases are divided based on specific patterns or conditions.

MergeRule class

The MergeRule class in NLP is used to define the rules for combining smaller chunks of text into larger, meaningful units. It allows for the reassembly of text by specifying conditions under which adjacent chunks should be merged based on predefined patterns or criteria.

Example of how the steps are performed

Here is the code implementation:

Constructing Tree

import nltk
from nltk.chunk import RegexpParser
from nltk.tree import Tree
from nltk.chunk.regexp import ChunkString, ChunkRule, MergeRule, SplitRule

# Define chunking rules using regular expressions
chunker = RegexpParser(r'''
  NP: {<DT><JJ>*<NN.*>}   # Noun phrase: Determiner + optional adjectives + Noun
  VP: {<VB.*><.*>*}        # Verb phrase: Verb + any following words
  PP: {<IN><DT><JJ>*<NN.*>} # Prepositional phrase: Preposition + Determiner + Noun
''')

# Example sentence with part-of-speech tagging
sent = [('The', 'DT'), ('quick', 'JJ'), ('brown', 'JJ'), ('dog', 'NN'),
        ('barked', 'VBD'), ('loudly', 'RB'), ('at', 'IN'), ('the', 'DT'),
        ('small', 'JJ'), ('cat', 'NN')]

# Chunking the sentence
tree = chunker.parse(sent)
print("Chunk Tree:", tree)
chunk_string.to_chunkstruct()

Output:

Splitting and Merging

# Loading Libraries for further chunking manipulation
chunk_string = ChunkString(Tree('S', sent))
print("Initial Chunk String:", chunk_string)

# Applying Chunk Rule (grouping determiners with nouns)
ur = ChunkRule('<DT><JJ>*<NN.*>', 'Chunk determiner + adjectives + noun')
ur.apply(chunk_string)
print("\nApplied ChunkRule:", chunk_string)

# Splitting based on nouns
sr1 = SplitRule('<NN.*>', '<.*>', 'Split after noun')
sr1.apply(chunk_string)
print("\nSplitting Chunk String (after noun):", chunk_string)

# Further splitting before determiners
sr2 = SplitRule('<.*>', '<DT>', 'Split before determiner')
sr2.apply(chunk_string)
print("\nFurther Splitting Chunk String (before determiner):", chunk_string)

# Merging similar noun chunks
mr = MergeRule('<NN.*>', '<NN.*>', 'Merge noun chunks')
mr.apply(chunk_string)
print("\nMerging Chunk String (nouns):", chunk_string)

# Back to Tree
chunk_string.to_chunkstruct()

Output:

Splitting and merging chunks in NLP allows for precise manipulation of text structure. The SplitRule and MergeRule classes enable efficient division and combination of text units based on predefined patterns, enhancing text analysis and interpretation.

mohit gupta_omg :)

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Article Tags :

• Machine Learning
• NLP
• AI-ML-DS
• Python-nltk
• Natural-language-processing
• python

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Practice Tags :

• Machine Learning
• python