AI is ultimately a representation of human intelligence, as such natural language processing (NLP) is part of the ai that describes the interaction between human language and computers. It is centered on giving computers the ability to understand human input such as text and spoken words with similar processes as humans.
NLP combines computational linguistics- algorithms based on the human language through machine learning, statistical and deep learning models. With all these combined it enables the computer the capability to process human inputs in the form of text and voice to form an understanding while retaining the intended and sentiment from the user.
NLP enables the computer programs from processing text to respond to spoken commands in real-time. As the years went by NLP is being used and implemented in GPS systems, speech-to-text software, digital assistance, chatbot, and so on. NLP is what helps in enterprise solutions to help businesses process.
Currently, there are 7,139 languages spoken today. Most people only use one to two languages in their daily lives, this shows the complexity behind the understanding of a language
and this is more apparent when attempting to use it to interact with one another. Interacting with one another includes variations, sarcasm, idioms, metaphors, and grammar in a sentence structure thus interacting with one another takes some years to learn. All the following also apply to writing software capable of understanding and determining the intended meaning of text or voice data. Programmers need to teach natural language-driven applications from the start and this process is over increasingly more complex as the application is more advance.
There includes multiple tasks in NLP to categorize and restructure them by breaking down the inputted info in a complex set of ways to help the computer to understand what is inputted. These tasks include:
Multiple languages contain words that have multiple meanings. Word sense disambiguation helps identify the intended meaning of the word in a sentence through a process called semantic mapping. Through this process, it can help distinguish verbs like “tree bark” vs “dog bark”.
Part of speech tagging involves determining a part of the speech category used in a sentence based on its use and context. The speech category that might be used includes adjective, conjunction, noun, pronoun, preposition, intersection, and so on.
This is used to analyze if the input is positive, negative, or neutral. This is often used by businesses to monitor customer feedback about their products and understand what is preferred and what is not. It is primarily focused on polarity, feelings, and intentions.
Natural language generation is a subset of NLP, it is the opposite of text to speech as it is designed to produce human language using structured information.
Named entity recognition allows the computer to recognize an entity to categorize them through NLP. In addition, it is to helps in identifying the core elements from a structured sentence such as places, brands, people, and so on.
The most common and basic use of NLP is email filtering. With years of development, machine learning is capable of separating and filtering emails, data samples into their designated inbox nowadays.
The most popular and well-known among them is Apple's Siri and Amazon's Alexa, they are virtual assistance that utilizes NLP technology to recognize patterns in voice commands and NLG to respond and process automatically.
Most people with access to the internet have used an internet browser before, as such the use of the browser search engine is inevitable. In a browser search, engine NLP machine learning is included, this helps analyze the search for related words and intention behind the inputted text using an advanced algorithm.
Text summarization includes the digestion of huge volumes of digital text to help reduce it and create a more concise version that retains its original intention/purpose.
Businesses have become increasingly reliant on NLP to gain data insights from social media channels. Sentiment analysis is being used to monitor sentiments on social media to analyze the overall product, brand, feature, and so on.