Automating the Mundane

NLP for Intelligent Document Processing

In today’s digital age, businesses are inundated with vast amounts of unstructured data contained within documents, emails, and other textual sources. Gaining meaningful insights from this data can be a laborious and time-consuming process. However, with the advent of Natural Language Processing (NLP), intelligent document processing has become a game-changer. In this blog post, we will delve into the world of NLP and explore how it enables businesses to automate mundane document-related tasks.

What is NLP?

Natural Language Processing (NLP) is a field of study that focuses on teaching computers to understand and interact with human language, just like we do. It’s a way for machines to read, interpret, and respond to text or spoken words in a way that makes sense to us. NLP helps computers understand the meaning behind words and sentences, allowing them to perform tasks like automatically answering questions, translating languages, summarizing documents, or even having conversations with us through chatbots or virtual assistants.

Think about how you communicate with others. You use words, sentences, and context to convey meaning, and NLP aims to teach computers to do the same. It involves teaching machines to recognize different parts of speech (like nouns, verbs, or adjectives), understand the relationships between words, and identify the main topics or sentiments expressed in a text.

Fig 1: Use cases for NLP in a business

How can NLP be used?

Organizations face many challenges when dealing with large volumes of documents, text and emails. Imagine you can automatically decide whether an email refers to a customer complaint or service query without reading the email, or you can automatically extract a client’s information such as their name or address from an invoice instead of having to manually find it. And what if you do not have to answer those frequently asked questions anymore? This is where the magic of NLP comes in.

NLP is used in various real-world applications. For example, it helps customer service chatbots understand and respond to your inquiries, assists in translating languages when you use online translation services, and powers voice assistants like Siri or Alexa that can answer questions (think ChatGPT) or perform tasks based on your spoken commands.

What are the challenges and shortcomings?

Data, Data, Data! NLP models require large amounts of high-quality data for training and fine-tuning. Generating and labeling data can be a time consuming and tedious task, but as the saying goes: ‘garbage in, garbage out’. The importance of a good quality data set cannot be underestimated. However, a lack of training data does not have to be a limiting factor and the use of unsupervised techniques and zero-shot models in the field of NLP has also shown good performance in some NLP tasks. There are also a wide variety of pretrained models available online (trained by the likes of Google and Facebook) that can be used and implemented as well as publicly available datasets that can be used to create an NLP model to fit your business needs.

Conclusion

The field of NLP is constantly evolving and improving. Intelligent document processing powered by NLP has the potential to transform how businesses handle their document-centric workflows. By automating mundane tasks such as document classification, data extraction, and sentiment analysis, NLP enables organizations to improve efficiency, accuracy, and overall productivity. Embrace the power of NLP today and unlock the benefits of intelligent document processing, streamlining operations and freeing up valuable human resources for more strategic tasks.