If you’ve ever used predictive text – the technology that enables words suggestions as you start typing a word – then you’ve experienced natural language processing (NLP). Although presenting word alternatives may sound simple, NLP is a very complex branch of artificial intelligence (AI). It does much more than give lists of (sometimes random) words. NLP allows computers to understand human language.
Of all the various flavors of AI, NLP is probably the one that people have the most exposure to even if they aren’t aware they are using it. NLP enables functions not just on your cellphone but also powers digital assistants like Amazon’s Alexa or guides and answers your questions when you call a contact center. That’s because NLP is what computers use to analyze, understand and derive meaning from human language in an intelligent and useful way.
What challenges do NLP tackle?
As far as computers are concerned, there are two types of data: structured and unstructured. Structured data is highly structured and organized – within databases or spreadsheets, for example – making it easily accessible for processing and analysis. Computers are great at handling structured data. However, it becomes a challenge for them to work with unstructured data – data that doesn’t have a pre-defined structure or format– such as human language.
Human language’s dynamics layer another functional challenge on top. It requires an understanding of its meaning for it to be used. This is something that humans are naturals at. We have years of experience in learning the nuances in speech and context. For example, if a friend told you that ‘Chicago destroyed Detroit last night’, you’d know they were talking about sports and not the start of a civil war. To make that distinction you not only recognize the words but understand the context of the phrase. NLP allows a computer to learn about the ambiguities and imprecise use of language – including things like slang and social context – to help it interpret the information in a ‘human-like’ manner.
How does Natural Language Processing work?
The process of reading and understanding language is far more complex than it seems at first glance. First, you must understand a language’s set of rules – grammar, for example – and then understand that there is no one set of rules for all languages but instead each language has their own. So, how does NLP help a computer understand language?
Each NLP system works slightly differently but the process is always similar. The system breaks each word down into its part of speech. For example, is it a noun or a verb? This is achieved through a series of grammar rules driven by algorithms to establish meaning and context. Key amongst these algorithms is semantic analysis that reduces sentences to their basic structure and looks for patterns to establish context. It is this analytical capability that enables the computer to understand that words can have different meanings and apply the correct meaning to a particular usage of the word.
Early approaches to NLP involved a highly rules-based approach, where machine learning algorithms were trained to look for specific words and phrases in text and to give specific responses when those phrases appeared. The algorithms were trained using large amounts of data to hone their ability and improve accuracy over time.
However, the growth of unstructured data within business communication and social media has proved the rule-based approach to be limited because data is based around free text where its accessibility and systematic analysis becomes challenging if not impossible. To overcome this, new generations of NLP models are based on deep learning technology that can access free text and identify and retrieve relevant information.
Deep learning, being a subset of machine learning, also looks for trends and patterns within unstructured data using neural networks to improve a computer’s understanding. This form of NLP requires massive amounts of information to identify the correlations and context within language but its flexible and intuitive approach produces highly accurate results and continually improves over time as more data becomes available to learn from.
How can you use NLP?
There is an ever growing number of use cases for NLP in business. These include:
NLP allows to quickly and easily search for relevant information within huge amounts of documents. Enterprise search allows the user to search for information by asking a question in the same way as they would to another person. The computer understands the question’s context and brings back results that only relate to the actual query.
Sentiment analysis applies NLP to understand the opinions and concerns of the people producing the content. While this approach can be applied to most forms of content, it is commonly used today to uncover insights in data from social media. Companies use sentiment analysis to understand what is being said about them and their products or services.
Automatic text summarization
NLP enables you to produce summaries of text documents. It searches within the document to locate relevant information and automatically creates a summary of the original document. The NLP system can take into account many variables such as grammar rules or the author’s writing style.
Text extraction uses NLP to extract structured information from unstructured data. For example, a company may receive an email from a customer and want to extract salient details from the content to feed into their CRM system. NLP can define the context and extract the relevant parts from any digital document.
Machine translation is the process of a computer translating one language to another. Google and other search engines base their machine translation technology on deep-learning NLP models. Anyone that has used services like Google Translate for a number of years will know how the continual learning process within NLP has improved the quality of the service.
More and more data is being created every day specifically using natural human language such as free text in social posts and emails. Until recently, companies couldn’t analyze this data and realize its business value. Today, NLP is making this possible, which is why the number of business cases being explored and discovered is increasing as well as the incorporation of NLP into other AI and analytic-driven disciplines such as predictive analytics and text mining.
To find out more about Natural Language Processing and other AI and analytics solutions from OpenText, visit our website.
Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.