Natural Language Processing (NLP) gives the machines the ability to read, understand and derive meaning from human languages. Nearly 90% of data generated today from various channels is unstructured such as email, social media, news feeds & blogs, text and OTT messages, audio, video and more. Some of the real-world applications of NLP include sentiment analysis, speech recognition, text summarization, machine translation, chatbots, etc. NLP involves developing algorithms and machine models to process, comprehend and generate human language.
NLP Use Cases:
NLP has diverse applications across industries due to its ability to extract insights from unstructured text data. Some prominent use cases include:
Information Extraction: NLP helps in extracting valuable information from large volumes of unstructured data, aiding in tasks like text summarization, entity recognition, and categorization.
Sentiment Analysis: NLP is utilized to analyze opinions, emotions, and sentiments expressed in social media posts, customer reviews, or surveys to understand public perception towards products or services.
Chatbots and Virtual Assistants: NLP powers intelligent chatbots and virtual assistants, allowing them to understand user queries and provide relevant responses, enhancing customer service and user experience.
Language Translation: NLP algorithms to translate text or speech from one language to another, enabling communication across linguistic barriers.
Speech Recognition: NLP technologies enable accurate speech recognition in applications like voice assistants, transcription services, and voice-controlled devices.
In this blog post we are going to focus on the use of the Bag of Words model for Natural Language Processing of (unstructured) text data for privacy use cases.
Enterprises employ NLP algorithms to automate and streamline high visibility, time sensitive, and labor intensive operations. In the privacy domain NLP is routinely used to identify Personally Identifiable Information (PII) and other sensitive data. Various enterprises in the Financial services such as Banks, FinTechs, Investment advisors, Insurance, etc., , Healthcare sector such as Hospitals, Pharmacies, Insurers, etc., Retail, Government services and others deal with PII and sensitive data.
NLP algorithms can be used to satisfy following use cases in the privacy management domain:
- Sensitive data classification: Identifies and categorizes documents containing PII and sensitive data.
- Sensitive data redaction: Redacts documents containing PII and sensitive data.
- Sensitive data deletion: Deletes documents containing PII and sensitive data to satisfy privacy regulations.
What is the Bag-of-Words model?
Bag-of-Words (BoW) is a statistical model used in NLP, particularly for textual input. BoW model relies on representation of text that captures multiplicity while disregarding order of words. BoW model can represent the input text for a text classification model. The model can then learn to predict the class label based on the presence or absence of certain words in the input text.
The Bag of Words (BoW) model is a straightforward and fundamental technique used for text representation in NLP. It involves the following steps:
Tokenization: Breaking down a piece of text into individual words or tokens.
Vocabulary Creation: Creating a vocabulary of unique words present in the entire corpus of documents.
Vectorization: Representing each document as a numerical vector, where each element corresponds to the frequency of a word in the vocabulary.
Using Bag of Words model for Text Classification and Sensitive data identification
The Bag of Words model can also be employed to identify sensitive data by creating a list of sensitive words or phrases. When processing text data, the model can flag or categorize documents that contain these predefined sensitive terms, enabling automated detection of potentially sensitive information.
In text classification, the Bag of Words model can be applied by following these steps:
Training Phase: Convert each document into a Bag of Words representation using tokenization and vectorization. Then, use these vectors as input to a machine learning algorithm for training the classification model.
Testing Phase: For new, unseen documents, transform them into Bag of Words vectors using the same vocabulary created during training. Feed these vectors into the trained model for classification into predefined categories or classes.
Implementing Bag of Words Model with PyTorch:
Using PyTorch for implementing a Bag of Words model involves the following steps:
Data Preparation: Gather and preprocess text data from various sources.
Tokenization and Vectorization: Tokenize text into words and create a vocabulary. Convert text into numerical vectors representing word frequencies.
Building the Model: Design a network architecture using PyTorch and implement layers for input, hidden, and output layers.
Training the Model: Train the model using the preprocessed data, adjusting weights through iterations to minimize error.
Evaluation and Inference: Assess the model’s performance on test data and use it for predicting text classifications.
In summary, Natural Language Processing plays a pivotal role in enabling machines to understand and work with human language. Natural Language Processing revolutionizes how machines interact with human language, offering solutions for automation, customer engagement, and data analysis. Combining techniques like Bag of Words models with powerful frameworks like PyTorch provide a foundational framework for text representation and analysis, facilitating various applications such as text classification and sensitive data identification applications across industries.
Additional references:
- Natural Language Processing (NLP) wikipedia: https://en.wikipedia.org/wiki/Natural_language_processing
- Sample implementation of BoW text classification: https://colab.research.google.com/github/scoutbee/pytorch-nlp-notebooks/blob/master/1_BoW_text_classification.ipynb#scrollTo=dRziGFdtQR8p