Last Updated on December 5, 2020 by Sean B
It’s common for the fields of studies of multiple domains to overlap with each other. This is the case for Natural Language Processing, or NLP, which is a subfield of Artificial Intelligence. Artificial Intelligence has spawned a rich discourse with its philosophical, linguistic, and cognitive implications. Natural Language Processing has implications across each of these areas since it involves the interpretation of the user input.
Since the very conception of Artificial intelligence, numerous issues and questions have been raised in academia. The question about consciousness, whether machines can think, and whether machines can understand human language!
This blog will provide a brief history of Natural language processing, how it works, how it’s used today, and its impacts on Artificial Intelligence.
What Is Natural Language Processing?
Natural Language Processing is a subfield of computer science, linguistics, and Artificial Intelligence that involves studying computer and human language interactions.
NLP is concerned with how computer programs understand and analyze vast amounts of natural language data. Essentially, Natural Language Processing is used to aid computers in the interpretation and manipulation of human language.
But Natural Language Processing has a goal to overcome the gap between human communication and artificial intelligence understanding. The various aspects of Natural language processing may include speech recognition, natural language understanding, and natural language generation.
A Brief History of Natural Language Processing
As surprising it may seem, the history of Natural Language Processing is relatively old and can date back to the 17th century where philosophers such as Rene Descartes and Gottfried Leibniz gave theoretical codes that could relate words with languages.
In the 1930s, the practical applications of such “translating machines” came into the spotlight when Georges Artsrouni proposed an automatic bilingual dictionary that could function using a paper tape.
In the 1950s, brilliant mathematician, crypto analyst, and computer scientist Alan Turing published his famous article “Computer Machinery and Artificial Intelligence” and proposed a widely applied test called initially called the Imitation Game, which is now called the Turing Test, that could be used to judge the intelligence of a machine.
The Turing test involved a computer participating in a conversation with a human agent. If the computer successfully tricked the human agent into thinking that he is, talking to another human and not a machine, the computer passes the Turing test.
In 1957, the linguistics field was revolutionized by the father of modern linguistics Noam Chomsky when he published his work “Syntactic Structures,” which provided the concept of “Universal Grammar.”
Similar turning points in the evolution of Natural Language Processing would continue to occur, for instance, the George Town experiment, the development of statistical machine translation, and augmented transition network (ATN) to represent natural language inputs.
The most significant revolution in Natural Language Processing was the introduction of machine learning algorithms in the 1980s. Combined with the huge growth in the available computational power that modern computers have, the developments in linguistics made it possible for the chatbots we have now to be developed.
Today, we have chatbots and virtual assistants that are exceptionally good at recognizing speech and language, such as Mitsuku, Cleverbot, Alexa from Amazon, Siri from Apple, and Cortana from Microsoft.
How Does Natural Language Processing Works?
Natural Language Processing works by applying algorithms that help recognize and extract the underlying rules of natural language and convert them into a form that the machines can understand.
Standard text in a raw form is provided to intelligent machines. Then they apply various algorithms to deconstruct the textual input and extract the underlying meaning behind the information and collect it as data. They use that to produce their response. With some chatbots, these responses are pulled from a list, but more advanced chatbots can produce their own responses based on the same or similar algorithms.
What Are the Common Tasks of Natural Language Processing?
Natural Language Processing can include various techniques to understand and interpret the contents of human language. This may consist of anything from statistical or machine learning methods to algorithmic and rule-based approaches.
Many of the tasks performed in NLP have popular real-world applications. The typical functions of NLP can be divided into the following categories.
Text and Speech Processing
The text and speech processing tasks in NLP can be divided into the following sub-categories.
Optical Character Recognition involves the recognition of text that is superimposed upon an image.
2. Speech-To-Text and Text-To-Speech Conversion
This consists of transforming the speech into text and vice versa.
This involves splitting the contents of speech or text into two or more two parts.
The morphological tasks of NLP can be divided into the following sub-categories.
Lemmatization involves reducing the various inflected forms of a word into a base dictionary form.
2. Morphological Segmentation
It consists of splitting words into individual units known as morphemes.
Stemming entails cutting the inflected words into their root forms!
4. Part-Of-Speech Tagging
This involves identifying the parts of speech for every word.
The portion of the syntactic tasks can be divided into the following sub-categories.
Parsing entails undertaking grammatical analysis for the provided sentence.
2. Sentence Breaking
Sentence breaking involves placing sentence boundaries on a large portion of text.
3. Grammar Induction
This involves generating a formal grammar that can describe the syntax behind a language.
Semantics is concerned with the meaning that is conveyed behind a text. The semantics involved in Natural Language Processing can be divided into the following sub-categories.
1. Named Only Recognition (NER)
Named only recognition involves identifying the parts of the text that can be separated into preset groups. These preset groups can include anything from names of people, buildings, locations, countries, characters, or any other kind of group.
2. Word Sense Disambiguation
Word sense disambiguation is used to give meanings to certain words based on the context.
3. Natural Language Generation
Natural language generation uses databases to derive semantic intentions and convert them into human language.
4. Relationship Extraction
This involves identifying the relationships among the named entities in any given text, e.g., Whether a certain someone is married to someone else.
Role of Natural Language Processing in Chatbot Technology
Since its development, Natural Language Processing has played a central role in developing and applying Artificial Intelligence and Chatbots; natural language processing ensures that the chatbot can interact with a human agent, whether via text or speech.
Chatbots capable of identifying, recognizing, and converting natural language are the most successful ones to stimulate an authentic conversation experience with the human agents.
The uses of natural language processing bots are not merely tied to conversations. Instead, they provide a plethora of services such as management, sales, businesses, finances, health sectors, and restaurants.
Here are the major benefits NLP provides to a chatbot:
1. Natural Conversations Regardless of Language
The problem with feeding static knowledge to your chatbot is the fact that languages are living phenomena; they are continually evolving and, by all means, are entirely dynamic. Furthermore, each language has many variations.
NLP can take these issues by making it possible for the bot to understand the sentences’ semantics, structures, and context. This increments the analyzing potential of the bot and prepares it for understanding human language more accurately.
2. Higher Customer Satisfactions
Today, people want a swifter way to carry out their interactions with corporations, and the bot that lacks the potential to communicate is unfit for the job. Luckily, NLP can help the chatbot understand and analyze the customers’ questions and extract meaning.
This makes the chatbot responses faster and more authentic and thus keeps the customer happy.
3. Reduction of Costs
NLP has another benefit of reducing costs for the firms that want to cut their expenses. NLP does this by cutting down the amount of labor force needed for a task by helping the chatbots specializing in any job on behalf of a human worker.
Some Famous NLP Chatbots
Here are some prominent examples of NLP chatbots.
1. ELIZA – The Psychotherapist
ELIZA is considered the first natural language processing chatbot ever made; it was created at MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.
ELIZA has inspired many other chatbots such as ALICE.
Jabberwacky was created by Rollo Carpenter in 1982. Jabberwacky has won the Loebner prize on more than one occasion, and an updated version of it, the Cleverbot, is available today on the internet.
3. Amazon ALEXA
is a virtual assistant AI technology developed by Amazon. She can voice interaction with users, setting alarms, playing music and audio books, making lists, showing news and weather reports, streaming podcasts, and performing a plethora of tasks. She is considered one of the best AI assistant of modern times.
4. Google Assistant
Google Assistant is another Virtual assistant AI technology that is developed by Google. Google Assistant can engage in two-way conversations. Users can talk to Google assistant either via voice or by typing through a keyboard.
Google Assistant can make searches on the internet, schedule alarms and events, show information on the account, and adjust the user device’s hardware settings
Natural Language Processing, or NLP, technology has made leaps and bounds since machine learning development and contributes to artificial intelligence and chatbots’ evolution. It is a subfield with extensions not only AI but also linguistics and computer science. As the name implies, it is the processing of natural language into a form that the computer can recognize, interpret, and manipulate.
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