How to use Natural Language Processing (NLP) is a component of Artificial Intelligence or AI. It is now becoming smarter in today’s world as technology keeps evolving. We see AI used for millions of purposes in a myriad of fields. Of course, it found its way into our Smartphones as even our mobile devices get progressively smarter. We communicate with AI on our Smartphones by using Natural Language Processing or NLP. But how do we use NLP?
Natural Language Processing is used for various purposes that allow Natural Languages to be transformed into usable data that AI can understand. That means that NLP is an AI branch that tackles how machines can interpret and understand human text and speech.
Considering that there are thousands of different natural languages today. It is difficult for machines to correctly interpret the meaning of words we are conveying and the messages we deliver. We are here to talk more about how natural language processing works to understand better how we use it regularly.
What is Natural Language?
Natural Language is the native speech of people. The language that people speak, such as English, Navajo, German, or Swahili, throughout the world
Natural Language, according to Wikipedia:
“In neuropsychology, linguistics, and the philosophy of language, a natural language or ordinary language is any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation. Natural languages can take different forms, such as speech or signing.”
Wikipedia – Natural Language
Language has always been the primary way for us to understand each other and talk to one another. Regardless of how different our vernacular may be, we always find a way to communicate effectively by translating our words or finding other means. We do this to effectively communicate with people who may not speak the same language as us.
What Is Natural Language Processing (NLP)?
So, what about artificial intelligence? How do we communicate with a machine that does not have the capacity to learn the languages we speak? How does it even get to understand thousands of different languages that are spoken throughout the world? It’s through Natural Language Processing or NLP.
Natural language processing is what helps machines and computers understand human languages regardless of whatever context is involved. This process allows devices to carry out different tasks for practical and industrial purposes without learning the languages we speak.
The ultimate goal of NLP is to find a way to decipher, analyze, and understand the meaning behind words. Analyze the sentences we are using so that the interaction between humans and machines will be improved tenfold. The mechanism is now technically capable of understanding the languages we are using and the context behind such languages.
We discuss AI for Beginners in our article “Smartphone AI: Helpful Artificial Intelligence For The Beginner.”
Basics Of Natural Language Processing
If you want to understand the way natural language processing works in the most basic of ways, here is what happens in the interaction between a human and a machine when NLP is involved:
- A human communicates with the machine by speaking or even by making use of texts.
- The machine receives the communication by capturing the audio or by receiving the texts that the human input.
- The computer converts the audio into text so that it can decipher and analyze whatever the human said.
- After that, the machine would now process the meaning of such words and the context in which they were used.
- Following the language processing, the machine would then convert the data it processed right back to audio or text.
- The machine finally responds to the human using the same language used in the audio or text. The information that the human inputted when interacting with the device.
How Does Natural Language Processing Work?
Now that you know how natural language processing operates in the most basic sense, you may be wondering how the processing works. How the machines can understand, decipher, process, and analyze whatever language the human uses when communicating with them.
In older mechanical ways, machines and computers understood what humans said through speech or text using the language’s keywords. The computers were technically programmed to receive specific keywords behind the speech or text. This was to make it easier for them to make sense of what a human is trying to say. This format also tends to be quite tedious as there are instances where the machine cannot decipher the meaning behind the language correctly.
Here comes NLP, which uses the cognitive approach when understanding human speech or text. Cognitive means, in this context, that machines can utilize what language is saying by understanding the meaning behind the words. This knowledge allows devices to know how the language is used to be more efficient in what they are programmed to do.
NLP Converts Language to Data
So, how does that even work because there are over 6,000 different languages in the world? NLP does not understand the words themselves initially, yet, in the true definition of human understanding. Instead, what happens here is that the words or the languages are transformed into something that the machine can understand, which is data.
As you saw above, the basics of NLP involve pre-processing the speech or text that the machines received or recorded by transforming them into data that they can utilize because data is the primary language used by computers.
Once the data has been prepared from the speech or text, the NLP algorithm kicks in to understand the context behind the words used in the speech or text. This process is where Machine Learning happens as the program uses a deep neural network. Then utilizes statistics to make sense of the meaning behind the words and what the human is trying to say. There are some cases where the algorithm is based on a rule that was programmed by the developer, who should be an expert in linguistics.
After that, the program will accurately interpret what the person was trying to say. At this point, they may even respond and feel as if they were interacting with an actual human.
Components of NLP
Natural Language Understanding (NLU)
- Mapping
- Analyzing
- Lexical, Syntactical, and Referential Ambiguity
Natural Language Generation (NLG)
- Text planning
- Sentence planning
- Text Realization
Some of the Processes of NLP
- Segmentation
- Tokenization
- Identifying Stop Words
- POS Tags
- Lemmatization
- Stemming
- Named Entity Recognition (NER)
- Chunking
How To Use NLP
Now that you know what NLP is all about, you might be asking how do we use NLP and in what context do we use NLP. There are plenty of use cases for NLP as it is so useful in many different fields.
For one, NLP has been used in the medical field as machines and computers can assess diseases based on the data of a person’s health records through a person’s speech patterns. As such, some researchers are exploring how NLP can be used to detect schizophrenia and depression.
Sentiment analysis is one of the more common ways we can use NLP. Natural Language Processing is also utilized in organizations to know what customers say about products through social media. NLP detects the sentiment behind the customer’s words to tell how customers feel about their products and what drives them to buy or not buy. This operation happens automatically without a human’s help as the machine itself uses NLP to decipher the sentiments.
These are just a few of the more common ways to use this technology as this area of artificial intelligence is still growing. We may even come to a point where our Natural Language Processing can understand the context behind our words and the meaning behind our tones to respond to us, similar to how humans do.
Natural Language Processing Applications
In the real world, we use NLP in a lot of different smartphone applications whether we are aware of them. Here are some of the more common places where NLP has been applied:
- Spam Filter – Companies such as Google apply NLP when filtering spam emails as their systems can effectively understand the meaning behind the emails.
- Smart Assistants – Smart assistants such as Google Assitant, Siri, and Alexa are widely used daily. Many utilize NLP so effectively that it seems as if you have an actual assistant helping you out at times.
- Search Engines – Search engines such as Google use NLP by knowing the context behind your searches. They are not merely giving you search results based on the words you are using. For example, if you put in a flight number on the search bar. Google will return to your search results based on that flight’s schedule and status. This is instead of merely giving you results based on the numbers you inputted.
- Language Translation – Language translation apps have gone a long way from a few years ago. These apps on our smartphones no longer try to translate sentences word per word. Instead, they now use NLP to translate what we are saying in a manner where context is considered. It will be easier for us to understand one another, especially if you travel to a different country.
We discuss useful NLP applications in our article “Smartphone AI: What Are Some Applications Of Applied Artificial Intelligence?”
Some NLP Application Examples Many That Are Free
- Speech Recognition – Virtual Assistants Google Assitant, Cortona, Siri, Language Translation Programs, Dictation Software, Telephone Customer Support Interaction
- Machine Translation – Google Translate, Microsoft Translator, Skype Translate, KantanMT, Lingua Custodia, DeepL
- Spelling and Grammar Checkers – Google Suite for Education, Microsoft Suites, Gmail, Apple Text, Grammarly
- Chatbot – Online Customer Service Chat Before Human Intervention
- Sentiment Analysis – Facebook, Twitter
- Search Autocorrect and Autocomplete – DuckDuckGo, Google, Bing, Yahoo
- Email Filtering – Outlook, Gmail, Yahoo Mail, Spamihilator
References:
https://web.stanford.edu/~jurafsky/slp3/
http://hci.stanford.edu/winograd/shrdlu/
https://www.aclweb.org/anthology/W09-0103.pdf
https://papers.nips.cc/paper/2015/file/277281aada22045c03945dcb2ca6f2ec-Paper.pdf