What is Natural Language Processing?

‍

Natural language processing (NLP) is a branch of technology that handles the interactions between natural human languages and machines. 

Natural language processing uses a combination of computational linguistics, machine learning, and deep learning models to train machines to understand speech and respond in much the same way as human beings would. In other words, the system can process and fully understand the input language (whether it be in text or voice format) and respond in a meaningful way by comprehending the writer’s intent and sentiment. 

Types of Natural Language Processing Tasks 

Natural language is filled with obscurities that make it difficult for machines to easily comprehend the meaning of the text or voice information provided. English, as an example, has many homonyms, idioms, metaphors, grammatical exceptions, homophones, and so on within its syntax. These intricacies are the reason it takes people years to learn a language and are what programmers must teach machines in order to make their interactions with their users meaningful. A simple thing as sarcasm can derail a software’s ability to decipher a person’s true intentions. 

There are a few components that encompass natural language processing systems:

• Speech recognition: As the name suggests, it is the task of converting voice data into text. The challenge with this is the challenge some people face when traveling to different countries: understanding the different ways people speak. Speaking quickly, using different accents, and slurring are examples of how a machine can run into issues when trying to understand the spoken dialogue.  
• Speech or grammatical tagging: The process of determining the meaning of a particular section of text based on its context. For example, tagging allows the computer to identify the difference in how the word ‘make’ is used within the sentence ‘I can make a cake’ (used as a verb) from the sentence ‘What make is your laptop’ (used as a noun). 
• Natural language generation (NLG): this is the action that converts structured information into natural human language and tends to be viewed as the opposite of speech recognition.

A Key Approach to Natural Language Processing

At its inception, it was nearly impossible for natural language processing applications to tend to a large volume of text and voice data due to a multitude of applications necessary to perform various rule-based natural language processing tasks needing to be hand-coding 

For this reason, programmers began implementing statistical natural language processing, combining algorithms with deep learning and machine learning paradigms to quickly and automatically scrape, classify and tag segments of text and voice data. Once this process is completed, a statistical likelihood of each meaning the text may have is assigned to the data. Combining these technologies allows systems to learn as they work, continuously extracting more and more meaning from more significant volumes of unlabelled text and voice datasets. 

Where is NLP Being Used 

• Chatbots and Virtual Assistants: Ever used Apple’s Siri or Amazon’s Alexa? These are two widely known cases in which speech recognition is being utilized to recognize patterns in spoken commands to respond with helpful remarks. Chatbots provide a somewhat similar level of assistance but via typed text. High-powered bots can learn contextual indicators regarding people’s requests to provide better responses over time.  

Despite chatbots’ somewhat impressive skills, their range of capabilities falls short compared to fully operational conversational AI systems. Read more about this technological gap on our chatbot versus conversational AI blog post.    

Language translation: The most used form of natural language processing is probably within translation systems such as Google Translate. A good translation doesn’t begin and end by simply taking a word and replacing it with one in another language. Effectively translating a text involves truly understanding the meaning of the input text (with all its possible nuances) and translating it to contain the same impact as the output language.