Artificial intelligence is transforming the way we plan and generate content. It’s also affecting how people discover material, from what they search for on Google to what they binge-watch on Netflix.
More crucially, for content marketers, it enables teams to grow by automating some types of content generation and analyzing current material to improve what you’re delivering and better match customer intent.
There are several moving pieces in the AI and machine learning processes. Have you ever asked a smart assistant (such as Siri or Alexa) a question?
The response is most likely “yes,” which suggests you are already acquainted with natural language processing on some level (NLP).
Alan Turing is a name that every techie has heard of. The well-known Turing Test was first devised in 1950 by renowned mathematician and computer scientist Alan Turing.
He claimed in his work Computing Machinery and Intelligence that a machine is artificially intelligent if it can converse with a person and deceive him into thinking he is chatting to a human.
This served as the basis for NLP technology. An efficient NLP system will be able to grasp the query and its context, analyze it, choose the best course of action, and answer in a language that the user will understand.
Worldwide standards for completing tasks on data include artificial intelligence and machine learning techniques. What about human language, though?
The fields of natural language generation (NLG), natural language understanding (NLU), and natural language processing (NLP) have all gained a lot of attention in recent years.
But because the three have different responsibilities, it is crucial to avoid confusion. Many believe they comprehend these ideas in their entirety.
Since natural language is already present in the names, all one is doing is processing, comprehending, and producing it. We decided it might be helpful to go a little bit deeper, though, given how frequently we encounter these phrases used interchangeably.
Consequently, let’s start by taking a close look at each of them.
What is Natural Language Processing?
Any natural language is considered to be a free-form text by computers. It follows that while entering data, there are no fixed keywords at fixed places. In addition to being unstructured, natural language also has a variety of expression options. Take these three phrases as an illustration:
- The weather is how is it today?
- Does today have any chance of rain?
- Does today require that I bring my umbrella?
Every one of these statements is asking about the weather prediction for today, which is the common denominator.
As humans, we can almost immediately see these fundamental connections and act appropriately.
However, this is a challenge for computers since every algorithm requires the input to follow a specific format, and all three statements have different structures and formats.
And things will get very difficult very soon if we try to codify rules for each and every word combination in every natural language to aid a computer in understanding. NLP steps into the picture in this situation.
Natural language processing (NLP), which tries to model natural human language data, originated from computational linguistics.
Additionally, NLP concentrates on using machine learning and deep learning approaches while processing a significant quantity of human input. It is frequently employed in philosophy, linguistics, computer science, information systems, and communications.
Computational linguistics, syntax analysis, speech recognition, machine translation, and other subfields of NLP are only a few. Natural language processing transforms unstructured material into the appropriate format or a structured text in order to function.
To comprehend what the user means when they say anything, it builds the algorithm and trains the model using vast quantities of data.
It operates by grouping distinct entities together for identification (known as entity recognition) and by recognizing word patterns. Lemmatization, tokenization, and stemming techniques are used to find the word patterns.
Information extraction, voice recognition, part-of-speech tagging, and parsing are just a few of the jobs that NLP does.
In the real world, NLP is used for tasks including ontology populating, language modeling, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, connection extraction, machine translation, and automated question answering.
What is Natural Language Understanding?
A minor portion of natural language processing is natural language comprehension. After the language has been simplified, the computer software must comprehend, deduce meaning, and possibly even carry out sentiment analysis.
The same text can have several meanings, several phrases can have the same meaning, or the meaning can change depending on the circumstance.
NLU algorithms use computational methods to process text from many sources in order to comprehend the input text, which can be as basic as knowing what a phrase means or as complicated as interpreting a conversation between two individuals.
Your text is transformed into a machine-readable format. As a consequence, NLU employs computational techniques to decipher the text and generate a result.
NLU can be applied in a variety of situations, such as comprehending a conversation between two people, determining how someone feels about a certain circumstance, and other situations of a like nature.
In particular, there are four language levels to grasp NLU:
- Syntax: This is the process of determining if the grammar is being utilized appropriately and how sentences are put together. For instance, a sentence’s context and grammar must be taken into account in order to determine if it makes sense.
- Semantics: When we examine the text, contextual meaning nuances like verb tenor or word choice between two persons are there. These bits of information can also be employed by an NLU algorithm to provide outcomes from any scenario in which the same spoken word could be used.
- Word sense disambiguation: It is the process of figuring out what each word in a phrase means. Depending on the context, it gives a term its meaning.
- Pragmatic analysis: It aids in comprehending the setting and purpose of the work.
NLU is significant to data scientists because, without it, they lack the ability to extract meaning from technologies like chatbots and speech recognition software.
After all, people are used to having a conversation with a speech-enabled bot; computers, on the other hand, do not have this luxury of ease.
In addition, NLU can recognize emotions and profanities in a speech exactly as you can. This implies that data scientists can usefully examine various content formats and classify text using the capabilities of NLU.
NLG works in direct opposition to natural language understanding, which aims to organize and make sense of unstructured data in order to convert it into useable data. Next, let’s define NLG and explore the ways data scientists use it in practical use cases.
What is Natural Language Generation?
Natural language processing also includes natural language production. Computers can write using natural language production, but natural language understanding focuses on reading comprehension.
By using certain data input, NLG creates a written answer in human language. Text-to-speech services can also be used to transform this text into speech.
When data scientists supply an NLG system with data, the system analyzes the data to produce narratives that can be comprehended through dialogue.
In essence, NLG converts data sets into a language that we both understand, called natural language. So that it can provide output that is carefully studied and accurate to the maximum extent feasible, NLG is endowed with the experience of a real-life human.
This method, which can be traced back to some of Alan Turing’s writings that we have already discussed, is crucial to convincing humans that a computer is conversing with them in a plausible and natural manner, regardless of the subject at hand.
NLG can be used by organizations to produce conversational narratives that can be used by everyone inside the company.
NLG, which is most frequently used for business intelligence dashboards, automated content production, and more effective data analysis, can be a big help to professionals working in divisions like marketing, human resources, sales, and information technology.
What role do NLU and NGL play in NLP?
NLP can be used by data scientists and artificial intelligence professionals to convert unstructured data sets into forms that computers can translate to speech and text – they can even construct replies that are contextually appropriate to a question you ask them (think back again to virtual assistants like Siri and Alexa).
But where do NLU and NLG fit into NLP?
Even though they all play different roles, all three of these disciplines have one thing in common: they all deal with natural language. So, what’s the distinction between the three?
Consider it this way: whereas NLU aims to comprehend the language that humans use, NLP identifies the most crucial data and organizes it into things like text and numbers.
It can even assist with harmful encrypted communications. NLG, on the other hand, uses collections of unstructured data to produce stories that we can interpret as meaningful.
Future of NLP
Although NLP has numerous current commercial uses, many businesses have found it difficult to adopt it broadly.
This is mostly because of the following issues: One issue that frequently affects organizations is information overload, which makes it challenging for them to identify which data sets are crucial amidst a seemingly unending sea of more data.
Additionally, in order to use NLP effectively, organizations frequently need certain methods and equipment that enable them to extract valuable information from data.
Last but not least, NLP implies that companies require cutting-edge machinery if they wish to handle and retain collections of data from various data sources utilizing NLP.
Despite obstacles keeping the bulk of firms from adopting NLP, it appears likely that these same organizations will ultimately embrace NLP, NLU, and NLG to enable their robots to sustain realistic, human-like interactions and discussions.
Semantics and syntax are two NLP subfields of research that are receiving a lot of attention.
Conclusion
Taking what we’ve discussed thus far into consideration: Assigning meaning to voice and writing, NLU reads and understands natural language, and NLG develops and outputs new language with the aid of machines.
Language is used by NLU to extract facts, whereas NLG uses the insights obtained by NLU to produce natural language.
Watch out for major players in the IT industry like Apple, Google, and Amazon to continue investing in NLP so they can develop systems that mimic human behavior.
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