Understanding how humans communicate with AI is a complex idea. Sending information or data to a machine and processing it in a way that helps the system fully understand the message passed and interpret it the right way to produce the right result. It is not an easy task to teach machines to understand how we communicate as humans. This is where Natural Language Processing comes in.
Leand Romaf, an experienced software engineer who is passionate about teaching people how artificial intelligence systems work, said: “In recent years, there have been significant breakthroughs in empowering computers to understand language just as we do.”
Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.
Most NLP techniques rely on machine learning to derive meaning from human languages.
The steps in Natural Language Processing are as follows:
- A human talks to the machine
- The machine captures the audio
- Audio to text conversion takes place
- Processing of the text’s data
- Data to audio conversion takes place
- The machine responds to the human by playing the audio file
What does NLP do?
Natural Language Processing is the driving force behind the following common applications:
- Language translation applications such as Google Translate helps to translate text and websites from one language into another.
- Word Processors such as Microsoft Word and Grammarly employ NLP to check the grammatical accuracy of texts.
- Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests.
- Personal assistant applications such as OK Google, Siri, Cortana, and Alexa used for voice commands.
How does NLP Work?
NLP involves applying algorithms to identify and extract the natural language rules in human communication in a way that allows the unstructured language data to be converted into a form that computers can understand and interpret.
When the text has been provided, the computer will utilize algorithms to extract meaning associated with every sentence and collect the essential data from them. Sometimes, the computer may fail to properly understand the meaning of a sentence which can lead to misinterpretation, yielding obscure results.
For example, a humorous incident occurred in the 1950s during the translation of some words between the English and the Russian languages.
Here is the biblical sentence that required translation:
“The spirit is willing, but the flesh is weak.”
Here is the result when the sentence was translated to Russian and back to English:
“The vodka is good, but the meat is rotten.”
How NLP can be used
In simple terms, NLP represents the automatic handling of natural human languages like written text or speech, fascinating as this may seem, the real value behind the NLP technology comes from how it is used in everyday life. NLP can help you in handling so many day-to-day tasks and the fields where it can be applied are continually increasing.
Let’s look at some ways you can use NLP:
NLP enables the recognition and prediction of diseases based on electronic health records and the patient’s own speech. This is constantly being used to detect health issues ranging from neurological and cardiovascular diseases to depression, anxiety, and even schizophrenia. For example, Amazon Comprehend Medical is a service that uses NLP to extract disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other electronic health records.
Organizations can determine what customers are saying about a service or product by identifying and extracting information from online sources. This monitoring and analysis can provide a lot of insight into how their customers think, what guides their buying process, and drives decision making.
An IBM inventor developed a cognitive assistant that works like a personalized search engine/ personal assistant by learning everything about you (your likes, dislikes, favorite places, and things, common routes, etc.) and then reminds you of a name, a song, or anything you can’t remember the moment you need it to.
Companies like Yahoo and Google filter and classify your emails with NLP by analyzing text in emails that flow through their servers and stopping spam before they even enter your inbox.
To help identify fake news, the NLP Group at MIT developed a new system to determine if a source is accurate or politically biased, detecting if a news source can be trusted or not.
Amazon’s Alexa and Apple’s Siri are examples of intelligent voice-driven interfaces that use NLP to respond to voice commands and take on daily instructional tasks like find the best route to your office or tell you the weather forecast, turn on the lights, play your favorite song or set a reminder for a particular event on a particular day.
In financial trading and stock exchange, predictions on the stock market can be made by NLP using trends and forecasts gotten from online research to suggest the best stock options to buy or sell at any given period.
NLP can also be used by recruiters to reach potential hires before they actively start searching for jobs. With predictions like online social learning and tracking professional certificates and other achievements, it can help you find the right fit for your organization before they actively get on the job market.
NLP is particularly sought after in the healthcare industry by automating medical records and doing for tasks in less time. In essence, it helps save on costs as well as improving accuracy, efficiency, and work productivity. The fact that clinical documentation can be improved means that patients can be better understood and benefited through better healthcare. The goal should be to optimize both patient and staff experience while increasing productivity, ensuring efficiency, and reducing errors to the barest minimum.
However, while all these strides are being made and technology has advanced to the point of NLP making people and organizations more efficient. There are some downsides to the use of NLP, the greatest being efficacy in understanding languages. The process of understanding and interpreting language is extremely complex, and for this reason, it is common to use different techniques to handle different challenges before binding everything together.
Programming languages like Python are highly used to perform these techniques, but it’s important to understand the concepts beneath them. It is also important to note than as advanced and effective as these languages are, they do not guarantee 100% interpretation of languages and the feelings it conveys. Taking into account things like sarcasm.
What are the techniques used in NLP?
Syntactic analysis and semantic analysis are the main techniques used to complete Natural Language Processing tasks.
Here is a description of how they can be used:
Syntax refers to the arrangement of words in a sentence such that they make grammatical sense. In NLP, syntactic analysis is used to assess how the natural language aligns with the grammatical rules. Computer algorithms are used to apply grammatical rules to a group of words and derive meaning from them.
Here are some techniques in syntactic analysis:
- Parsing: This involves grammatically analyzing a provided sentence.
- Morphological segmentation: This takes words and divides them into individual units called morphemes
- Word segmentation: This takes a large piece of continuous text and divides it into smaller units.
- Lemmatization: This entails reducing the various inflections of a word and turning them into a single form for easy analysis.
- Part-of-speech tagging: This identifies the proper part of speech for every word.
- Stemming: This is cutting each inflected word to its root form.
- Sentence breaking: This is placing proper sentence boundaries on a large piece of text.
This refers to the meaning conveyed by a text. It is one of the more difficult aspects of Natural Language Processing and so far has still not been resolved. It works by applying computer algorithms to understand and interpret the meaning of a text or spoken words and how sentences are structured.
Here are some techniques in semantic analysis:
- Named entity recognition (NER): This decides on what parts of a text can be identified and categorized into preset groups. For instance, names of places, people and things
- Word sense disambiguation: This is giving meaning to a word based on the context in which it is written or spoken.
- Natural language generation: This uses databases to derive semantic intentions and convert them into human language.
Just how difficult is NLP?
Natural Language processing is considered a fairly difficult aspect of computer science and artificial intelligence. This is due to the nature and diversity of human languages. The rules that guide the passing of information using natural languages are not easy for computers to understand or interpret. Some of these rules can be high-level and abstract, for example, when someone uses a sarcastic remark to pass information. Or low-level, for example, using the character “s” to signify the plurality of items.
A comprehensive understanding of the human language requires understanding both the words written or spoken and how they are connected to deliver the intended message. While humans can easily master languages, the ambiguous and imprecise nature of natural languages is what makes NLP difficult for machines to implement.