What is NLP?
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI). It enables robots to analyze and comprehend human language, enabling them to carry out repetitive activities without human intervention. Examples include machine translation, summarization, ticket classification, and spell check.
Natural language processing (NLP) is the ability of a computer to analyze and understand human language. NLP is a subset of artificial intelligence focused on human language and is closely related to computational linguistics, which focuses more on statistical and formal approaches to understanding language.
NLP is typically used for document summarization, text classification, topic detection and tracking, machine translation, speech recognition, and much more.
How NLP Works?
NLP systems use machine learning algorithms to analyze large amounts of unstructured data and extract relevant information. The algorithms are trained to recognize patterns and make inferences based on those patterns. Here's how it works:
- The user must input a sentence into the Natural Language Processing (NLP) system.
- The NLP system then breaks down the sentence into smaller parts of words, called tokens, and converts audio to text.
- Then, the machine processes the text data and creates an audio file based on the processed data.
- The machine responds with an audio file based on processed text data.
NLP Market Size & Growth
Artificial intelligence stands to be the next big thing in the tech world. With its ability to understand human behavior and act accordingly, AI has already become an integral part of our daily lives. The use of AI has evolved, with the latest wave being natural language processing (NLP).
The global NLP market size is valued at USD 15.7 billion in 2022 and is expected to grow at a CAGR of more than 25% over the forecast period 2022-2027. The market is projected to reach 49.4 billion USD by 2027 at a CAGR of 25.7%.
Benefits of NLP
Increased documentation efficiency & accuracy
An NLP-generated document accurately summarizes any original text that humans can’t automatically generate. Also, it can carry out repetitive tasks such as analyzing large chunks of data to improve human efficiency.
Capability to automatically create a summary of large & complex textual content
Natural processing language can be used for simple text mining tasks such as extracting facts from documents, analyzing sentiment, or identifying named entities. Natural processing can also be used for more complex tasks, such as understanding human behaviors and emotions.
Enables personal assistants like Alexa to interpret spoken words
NLP is useful for personal assistants such as Alexa, enabling the virtual assistant to understand spoken word commands. It also helps to quickly find relevant information from databases containing millions of documents in seconds.
Enables the usage of chatbots for customer assistance
NLP can be used in chatbots and computer programs that use artificial intelligence to communicate with people through text or voice. The chatbot uses NLP to understand what the person is typing and respond appropriately. They also enable an organization to provide 24/7 customer support across multiple channels.
Performing sentiment analysis is simpler
Sentiment Analysis is a process that involves analyzing a set of documents (such as reviews or tweets) concerning their attitude or emotional state (e.g., joy, anger). Sentiment analysis can be used for categorizing and classifying social media posts or other text into several categories: positive, negative, or neutral.
Advanced analytics insights that were previously out of reach
The recent proliferation of sensors and Internet-connected devices has led to an explosion in the volume and variety of data generated. As a result, many organizations leverage NLP to make sense of their data to drive better business decisions.
Challenges with NLP
Misspellings
Natural languages are full of misspellings, typos, and inconsistencies in style. For example, the word “process” can be spelled as either “process” or “processing.” The problem is compounded when you add accents or other characters that are not in your dictionary.
Language Differences
An English speaker might say, “I’m going to work tomorrow morning,” while an Italian speaker would say, “Domani Mattina vado al lavoro.” Even though these two sentences mean the same thing, NLP won’t understand the latter unless you translate it into English first.
Innate Biases
Natural processing languages are based on human logic and data sets. In some situations, NLP systems may carry out the biases of their programmers or the data sets they use. It can also sometimes interpret the context differently due to innate biases, leading to inaccurate results.
Words with Multiple Meanings
NLP is based on the assumption that language is precise and unambiguous. In reality, language is neither precise nor unambiguous. Many words have multiple meanings and can be used in different ways. For example, when we say “bark,” it can either be dog bark or tree bark.
Uncertainty and False Positives
False positives occur when the NLP detects a term that should be understandable but can’t be replied to properly. The goal is to create an NLP system that can identify its limitations and clear up confusion by using questions or hints.
Training Data
One of the biggest challenges with natural processing language is inaccurate training data. The more training data you have, the better your results will be. If you give the system incorrect or biased data, it will either learn the wrong things or learn inefficiently.
NLP Example
Natural Language translation i.e., Google Translate
Google Translate is a free web-based translation service that supports over 100 languages and can translate your content automatically into these languages. The service has two modes: translation and translation suggestions.
Word Processors i.e., MS Word & Grammarly use NLP to check grammatical errors
Word processors like MS Word and Grammarly use NLP to check text for grammatical errors. They do this by looking at the context of your sentence instead of just the words themselves.
Speech recognition / IVR systems used in call centers
Speech recognition is an excellent example of how NLP can be used to improve the customer experience. It is a very common requirement for businesses to have IVR systems in place so that customers can interact with their products and services without having to speak to a live person. This allows them to handle more calls but also helps cut costs.
Personal Digital Assistants i.e., Google Home, Siri, Cortana, & Alexa
The use of NLP has become more prevalent in recent years as technology has advanced. Personal Digital Assistant applications such as Google Home, Siri, Cortana, and Alexa have all been updated with NLP capabilities. These devices use NLP to understand human speech and respond appropriately.
Use Cases
Intelligent document processing
This use case involves extracting information from unstructured data, such as text and images. NLP can be used to identify the most relevant parts of those documents and present them in an organized manner.
Sentiment Analysis
Sentiment analysis is another way companies could use NLP in their operations. The software would analyze social media posts about a business or product to determine whether people think positively or negatively about it.
Fraud detection
NLP can also be used for fraud detection by analyzing unstructured data like emails, phone calls, etc., and insurance databases to identify patterns or fraudulent activities based on keywords.
Language detection
NLP is used for detecting the language of text documents or tweets. This could be useful for content moderation and content translation companies.
Conversational AI / Chatbot
A conversational AI (often called a chatbot) is an application that understands natural language input, either spoken or written, and performs a specified action. A conversational interface can be used for customer service, sales, or entertainment purposes.
Text summarization
An NLP system can be trained to summarize the text more readably than the original text. This is useful for articles and other lengthy texts where users may not want to spend time reading the entire article or document.
Text Translation
NLP is used for automatically translating text from one language into another using deep learning methods like recurrent neural networks or convolutional neural networks.
Question-Answering
Question answering (QA) is a task in natural language processing (NLP) that receives a question as input and returns its answer. The simplest form of question answering is to find a matching entry in the knowledge base and return its contents, known as “document retrieval” or “information retrieval.”
Named Entity Recognition
Named entity recognition is a core capability in Natural Language Processing (NLP). It’s a process of extracting named entities from unstructured text into predefined categories. Examples of named entities include people, organizations, and locations.
Social Media Monitoring
Social media monitoring tools can use NLP techniques to extract mentions of a brand, product, or service from social media posts. Once detected, these mentions can be analyzed for sentiment, engagement, and other metrics. This information can then inform marketing strategies or evaluate their effectiveness.
Predictive Text
The predictive text uses NLP to predict what word users will type next based on what they have typed in their message. This reduces the number of keystrokes needed for users to complete their messages and improves their user experience by increasing the speed at which they can type and send messages.
