preprocess-nlp 0.1

A fast framework for pre-processing (Cleaning text, Reduction of vocabulary, Feature extraction and Vectorization). Implemented with parallel processing using custom number of processes.

Preprocess NLP Text

Framework Description

A simple and fast framework for

• Preprocessing or Cleaning of text
• Extracting top words or reduction of vocabulary
• Feature Extraction
• Word Vectorization

Uses parallel execution by leveraging the multiprocessing library in Python for cleaning of text, extracting top words and feature extraction modules. Contains both sequential and parallel ways (For less CPU intensive processes) for preprocessing text with an option of user-defined number of processes.

PS: There is no multi-processing support for word vectorization

• Cleaning Text - Clean text with various defined stages implemented using standardized techniques in Natural Language Processing (NLP)
• Vocab Reduction - Find the top words in the corpus, lets you choose a threshold to consider the words that can stay in the corpus and replaces the others
• Feature Extraction - Extract features from corpus of text using SpaCy
• Word Vectorization - Simple code to convert words to vectors (TFIDF, Word2Vec, GloVe) using Scikit-learn and Gensim

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Preprocess/Cleaning Module

Uses nltk for few of the stages defined below. Various stages of cleaning include:

Stage	Description
remove_tags_nonascii	Remove HTML tags, emails, URLs, non-ascii characters and converts accented characters
lower_case	Converts the text to lower_case
expand_contractions	Expands the word contractions
remove_punctuation	Remove punctuation from text, but sentences are seperated by ' . '
remove_esacape_chars	Remove escapse characters like \n, \t etc
remove_stopwords	Remove stopwords using nltk python
remove_numbers	Remove all digits in the text
lemmatize	Uses WordNetLemmatizer to lemmatize text
stemming	Uses SnowballStemmer for stemming of text
min_word_len	Minimum word length to keep in text

Reduction of Vocabulary

Shortlists top words based on the percentage as input. Replaces the words not shortlisted and replaces them efficienctly. Also, supports parallel and sequential processing.

Feature Extraction Module

Uses Spacy Pipe module to avoid unnecessary parsing to increase speed. Various stages of feature extraction include:

Stage	Description
nouns	Extract the list of Nouns from the given string
verbs	Extract the list of Verbs from the given string
adjs	Extract the list of Adjectives from the given string
noun_phrases	Extract the list of Noun Phrases (Noun chunks) from the given string
keywords	Uses YAKE for extracting keywords from text
ner	Extracts Person, Location and Organization as named entities
numbers	Extracts all digits in the text

Word Vectorization

Functions written in python to convert words to vectors using libraries like Scikit-Learn and Gensim. Contains four vectorization techniques like CountVectorizer (Bag of Words Model), TFIDF-Vectorizer, Word2Vec and GloVe. Also contains others features to get the top words according to IDF Scores, similar words with similarity scores and average sentence-wise vectors.

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Code - Components

Various Python files and their purposes are mentioned here:

• preprocess_nlp.py - Contains functions which are built around existing techniques for preprocessing or cleaning text. Defines both sequential and parallel ways of code execution for preprocessing.
• Preprocessing_Example_Notebook.ipynb - How-to-use example notebook for preprocessing or cleaning stages
• requirements.txt - Required libraries to run the project
• vocab_elimination_nlp.py - Contains functions which are built around existing techniques for shortlisting top words and reducing vocab size
• Vocab_Elimination_Example_Notebook.ipynb - How-to-use example notebook for vocabulary reduction/elimination or replacement.
• vectorization_nlp.py - Contains functions which are built around existing techniques for vectorizing words.
• Vectorization_Example_Notebook.ipynb - How-to-use example notebook for vectorization of words and additional functions or features.

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How to run

1. pip install -r requirements.txt
2. Import preprocess_nlp.py and use the functions preprocess_nlp(for sequential) and asyn_call_preprocess(for parallel) as defined in notebook
3. Import vocab_elimination_nlp.py and use functions as defined in the notebook Vocab_Elimination_Example_Notebook.ipynb
4. Import feature_extraction.py and use functions as defined in notebook Feature_Extraction_Example_Notebook.ipynb
5. Import vectorization_nlp.py and use functions as defined in notebook Vectorization_Example_Notebook.ipynb

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Sequential & Parallel Processing

1. Sequential - Processes records in a sequential order, does not consume a lot of CPU Memory but is slower compared to Parallel processing
2. Parallel - Can create multiple processes (customizable/user-defined) to preprocess text parallelly, Memory intensive and faster

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Refer the code for Docstrings and other function related documentation.
Cheers :)