On average, a human speaks 700 words per day, which includes the words of actual value (i.e. words that have an intent to communicate to another person). Instant messaging, telephonic conversations, books, movies, songs, verbal (F2F) communication are amongst some of the common ways humans exchange their thoughts and information.
According to the Wernicke-Lichtheim-Geschwind model, the human brain perceives the words through a specialized word reception center called Wernicke’s area, which is located in the left temporoparietal junction. Wernicke’s region then projects to Broca’s area of the brain which is called the word production center located in the frontal lobe. So when someone asks “How are you?”, the information is received by Wernicke’s area for interpretation, and further, it is Broca’s area that selects the words for a response- “I am fine, thanks!”
The human brain is supported by millions of neurons to comprehend words and take action. Ever wondered how machines process human language and respond back with the right set of words. For example, when you talk to your voice assistants, say Amazon Alexa; how it understands your words, intent, and gives a response which is quite satisfactory as well.
Well, the technology behind machines learning to talk like humans is Natural Language Processing (NLP). The technology is a subset of Artificial Intelligence that deals with the interaction between humans and machines. NLP provides machines with the ability to process what is said, structure the information received, discover the necessary response, and then respond in the language that a user understands.
Voice assistants are not just one amazing use case of Natural Language Processing. This technology has a plethora of applications and in the latter segment of this blog, we are going to discuss them. Let’s get started.
1. Machine Translation
Machine Translation is the subfield of computer linguistics which involves the use of software applications to translate text or speech from one language to another. One of the best examples of machine translation is Google Translate which is known to translate over 100 billion words every day.
Google Translate uses neural machine translation which uses artificial neural networks (deep learning) to translate the whole sentence at a time. It uses the example-based machine translation approach wherein the system learns from millions of examples to improve the output.
However, the ambiguity of human language makes it difficult for the system to understand the words, sentences, and their intent. Natural Language Processing helps in overcoming this challenge.
2. Conversational User Interface
A conversational user interface is an interface for computers that emulates a conversation with a real human. For example, a chatbot.
Chatbot provides an interface that enables machines and users to communicate through text. The history and evolution of chatbots have been impressive. From performing the role of a customer service agent with a predefined set of Q/A to becoming a mobile app alternative, chatbots have certainly come a long way.
A chatbot is a text-based CUI that can act as a Q/A platform, allow users to place orders, give the status of their orders, sort information, book flight tickets, financial transactions, improve marketing campaigns, and a lot more. For performing complex tasks, a chatbot needs to understand user input, interpret it, and respond accordingly. This is where NLP plays an important role.
3. Text Prediction
Text prediction refers to the process of estimating the next word in a phrase or sentence. One of the popular and common examples of text prediction is Google Search.
Google uses BERT (Bidirectional Encoder Representations from Transformers), a Natural Language Processing (NLP) algorithm that uses neural networks to create pre-trained models. These models are trained using a huge amount of unannotated text available on the web. The BERT algorithm helps the search engine to understand queries in a way similar to humans. Other than this, many other applications such as Google Docs, Gmail Compose uses NLP language models that help in text prediction.
4. Sentiment Analysis
Sentiment analysis is the process of interpreting and classifying emotions within text data. Usually having business-specific applications, sentiment analysis allows businesses to identify the sentiment of a customer towards services, brands, or products in online feedback (positive, negative, neutral). For its exceptional abilities, sentiment analysis is used in product analytics, market research, reputation management, precision targeting, marketing analysis, public relations, net promoter scoring, etc.
5. Text Classification
Unstructured text is everywhere. Emails, social media, websites, chat conversations are some of the areas where NLP text classification is an essential component and is rigorously used.
Text classification algorithms lay down the basis for software systems that process text data on a large scale. For example, email clients use text classification for tagging emails to certain categories so that they can be sent to the inbox or spam folder. Moreover, the automatic categorization of emails as Primary, Social, or Promotions in Gmail software is one of the finest examples of text classification using NLP.
Another area where text classification has a qualitative application is Discussion Forums. In such areas, text classification algorithms determine if a comment needs to be flagged as inappropriate. Along with this, platforms such as eCommerce, news agencies, content curators, etc. use text classifiers to automate content/product/service tagging.
5. Spell Check
A spell checker is a software application that identifies and corrects spelling mistakes or typos in a text. One of the popular examples of spell checker software is Grammarly. It’s a tool that integrates with a variety of text documents, automatically checks for spelling errors as the user continues to write, and suggest corrections.
While Spell Checkers have immense importance in software applications that create and format word documents, they are also an important part of websites search wherein there are high chances of users doing spelling errors or typos, which may affect the search results.
6. Speech Recognition
The speech recognition technology has been there for around 70 years. The first-ever speech recognition system was introduced in 1952 by Bell Laboratories. Known as “Audrey”, the system could recognize a single-digit number. Later, IBM introduced “Shoebox” which could understand and respond to 16 words in English, which marked the usage of Natural Language Processing (NLP) for speech recognition.
Today, the advances in NLP has introduced voice as the method of providing inputs to the system, other than clicking, typing, or selecting text. Voice Assistants like Cortana, Siri, Google Assistance, Amazon Alexa are some of the perfect examples of how machines are mastered to recognize the human voice, understand the intent, and respond accordingly. In fact, NLP is the core technology behind the Voice User Interface today.
Another common example of speech recognition is the speech-to-text feature in smartphones. While this is a default feature in many smartphones, there are several mobile apps that make this possible for users. The speech-to-text facility enables users to provide input through audio which is then converted into text.
Google Voice Search is another example of speech recognition wherein the users provide voice input to perform the search.
7. Character Recognition
Optical Character Recognition (OCR) is the process of converting images of handwritten, typed, or printed text into machine-encoded language. It is one of the commonly used approaches to digitize printed texts so it can be saved, edited, searched electronically.
OCR and NLP combine to provide a range of benefits that can help businesses complete their document-related tasks such as read and recognize ID cards/passports automatically, enter data into various forms or CRMs, verify customer data gathered from various sources, instantly scan bank cards, indices, cheques, tickets, etc.
Natural Language Processing (NLP): Challenges and How to Overcome Them
The ambiguity associated with human language is one of the biggest challenges associated with Natural Language Processing (NLP). Therefore, understanding the requirement, training the models & algorithms right, and then combining the NLP technology with other AI technologies is the key to overcome the challenges associated with NLP.
At Daffodil, our AI experts understand the AI technologies and discover the most appropriate one for a project. Thus, if you have an idea where the implementation of AI technologies seems to be a challenge or you need technical aid to get started with it, set up a free consultation session with our AI application development experts.