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What is Natural Language Processing?
Natural Language Processing a subset technique of Artificial Intelligence which is used to narrow the communication gap between the Computer and Human. It is originated from the idea of Machine Translation (MT) which came to existence during the second world war. The primary idea was to convert one human language to another human language, for example, turning the Russian language to English language using the brain of the Computers but after that, the thought of conversion of human language to computer language and vice-versa emerged, so that communication with the machine became easy.
In simple words, a language can be understood as a group of rules or symbol. These symbols are integrated and then used for transmitting as well as broadcasting the information. Here rules are applied to suppress the symbols. The area of Natural Language Processing is divided into sub-areas, i.e., Natural Language Generation and Natural Language Understanding which are as the name suggest associated with the generation and understanding the text. The following chart broadly shows these points Don't get confused by these new terms such as Phonology, Pragmatics, Morphology, Syntax, and Semantics. Let's explore these in a very brief manner-
- Phonology - This science helps to deal with patterns present in the sound and speeches related to the sound as a physical entity.
- Pragmatics - This science studies the different uses of language.
- Morphology - This science deals with the structure of the words and the systematic relations between them.
- Syntax - This science deal with the structure of the sentences.
- Semantics - This science deals with the literal meaning of the words, phrases as well as sentences.
The ability of machines to understand and interpret human language the way it is written or spoken. Click to explore about, NLP Techniques and Applications
What is the History of Natural Language Processing?
History of natural language processing are described:
The Beginning
As stated above the idea had emerged from the need for Machine Translation in the 1940s. Then the original language was English and Russian. But the use of other words such as Chinese also came into existence in the initial period of the 1960s. Then a lousy era came for MT/NLP during 1966, this fact was supported by a report of ALPAC, according to which almost died because the research in this area did not have the pace at that time. This condition became better again in the 1980s when the product related to it started providing some results to customers. After reaching in dying state in the 1960s, the got a new life when the idea and need of Artificial Intelligence emerged
LUNAR is developed in 1978 by W.A woods; it could analyze, compare and evaluate the chemical data on a lunar rock and soil composition that was accumulating as a result of Apollo moon missions and can answer the related question. In the 1980s the area of computational grammar became a very active field of research which was linked with the science of reasoning for meaning and considering the user ‘s beliefs and intentions. In the period of 1990s, the pace of growth of it increased. Grammars, tools and Practical resources related to it became available with the parsers.
The research on the core and futuristic topics such as word sense disambiguation and statistically colored NLP, the work on the lexicon got a direction of research. This quest of the emergence of it was joined by other essential topics such as statistical language processing, Information Extraction and automatic summarising.
The First Chatbot - ELIZA
The discussion on the history cannot be considered complete without the mention of the ELIZA, a chatbot program which was developed from 1964 to 1966 at the Artificial Intelligence Laboratory of MIT. It was created by Joseph Weizenbaum. It was a program which was based on script named as DOCTOR which was arranged to Rogerian Psychotherapist and used rules, to response the questions of the users which were psychometric-based. It was one of the chatbots which were capable of taking the Turing test at that time.
The Government is taking modern emerging technologies in its architecture gradually. Click to explore about, Role and Uses of NLP in Government
What are the Applications of Natural Language Processing?
Nowadays everybody wants the machine to talk, and the only way by which a computer can speak is through it. Take the example of Alexa, a conversational product by Amazon. A query is passed to it by the medium of voice, and it can reply by the same medium, i.e., voice. It can be used to ask anything, search for anything, for playing songs or even for cab booking. It seems to be magic, but it is not because of any magic spell, see the below diagram. This simple diagram is the demonstration of the procedure of Natural Language Processing in Alexa.
Alexa is not a single example, and these talking machines which are popularly known as Chatbot can even manage complicated interactions and the processes related to the streamlined business using it only. In the past chatbot were utilized for only customer interaction with limited capabilities of conversation because they were generally rule-based but after the emergence of Natural Language processing and its integration with Machine Learning and Deep Learning, now chatbot can handle many different areas such Human Resources and Health. This is not the only use case of it where it emerges as a game changer; there are other examples also. Let’s have a brief look at them. Below is the description of some use cases.
- Health Care
- Sentiment Analysis
- Cognitive Analytics
- Spam Detection
- Recruitment
- Conversational Framework
Health Care
Amazon Comprehend Medical services which are used to extract the disease conditions, can handle meditations sessions and can monitor the results of the treatment using clinical trial reports, electronic health records and using patient notes. This is an example of NLP in health analytics where using Language processing the prediction of different diseases is possible using pattern recognition methods and patient ‘s speech and their electronic health record.
Cognitive Analytics
This is the best example of the collaboration of different technologies, but both come under the same roof of Artificial Intelligence. The conversational frameworks are possible which can take commands by the medium of voice or by the medium of text. Using cognitive analytics, the automation of different technical processes are possible now such generation of a technical ticket related to a technical issue and also handling it in automated or semi-automated ways. The collaboration of these techniques can result in an automated process of handling technical issues inside an organization or providing the solution of some technical problems to the customer also in an automated manner.
Simulate the human thought process to learn from the data and extract the hidden patterns from data. Source: Cognitive Analytics Tools and its Applications
Sentiment Analysis
The companies and organizations are now concentrating on the different ways to know their customers so that a personalized touch can be provided. Using sentiment analysis (which is possible only using NLP) the sentiments behind the words can be determined. The sentiment analysishas the capabilities to offer a lot of knowledge about the customer's behavior and their choices which can be considered as significant decision drivers.
Spam Detection
The giants of the technical world such as Google and Yahoo use NLP to classify and filter out the emails which are suspected to be spam. This process is known as Spam Detection and Spam Filtering respectively. It results in an automated process which can classify the email as spam and stop it for entering the Inbox.
Recruitment
It also be used in both search and selection phases of Job Recruitment, in fact, the chatbot can also be used to handle the job-related query at Initial level which also includes identifying the required skills for a specific job and handling initial level tests and exams.
Conversational Framework
This technology and the devices related to it are gaining so much popularity these days. Alexa which was illustrated above is one of them, but there are Apple's Siri and Google‘s Ok Google which are the examples of the same sort of technology use cases.
A subset of Artificial Intelligence increasing its necessity with the improvement of its sub-technologies day by day. Click to explore about, Applications of NLP For Businesses
Top 9 Natural Language Processing Trends
Maturing technology is helping create more innovative NLP systems. Below are some key trends that are pivotal in shaping the future of NLP systems. Help shape the future of NLP systems.
Smarter Service Desk Responses
Today, when a user contacts a service desk with a problem, they frequently receive a response in the form of a ticket that has been opened, and they will receive a response within a certain amount of time. However, according to research, most tickets are repetitive and can be resolved automatically if organizational knowledge is mined correctly. Natural Language Understanding (NLU) can be a considerable asset and be used quickly and automatically to solve the problem. After analyzing previous interactions based on the subject, content, and ticket category, the system will decide on the final resolution mechanism.
The NLP system's initial reaction will be to provide the user with a clear action plan. It will then follow up on the user's email with a virtual assistant who can aid in solving their problem right away. Response emails will become more knowledgeable in this way, which will enhance the customer's relationship with the brand.
Improvements in Enterprise Search
Natural Language Processing can enhance the capabilities of enterprise software solutions. Most enterprise solutions collect and use a huge amount of data for everything from customer service to accounting. Many organizations invest significant resources to store, process, and get insights from these data sources. But key insights and organizational knowledge may be lost within terabytes of unstructured data.
NLP improves team members and customer experience with enterprise software. It increases the interaction between the user and the software.
Enterprise Experimenting NLG
Natural Language Generation (NLG) sub-part of NLP. It uses AI to produce narratives from the dataset. It has six stages - Examining the content, Data comprehension, Document structuring, Sentence aggregation, Grammatical organization, and Language Presentation. Data filtration by identifying the main topics to include at the process's end. Data interpretation and understanding are accomplished through machine learning. A written plan is made based on the type of data interpreted.
Voice-driven Navigation Assistants
The voice control technology is used in several different segments of products and services. Especially in cars, the technology is used to help achieve hand-free capabilities as drivers rely on an in-car voice assistant to accomplish many functions. Some primary tasks that can be accomplished using voice control technology include setting navigation. Receiving hands-free calls, placing restaurant orders, controlling in-car temperatures, windshield wiper operation, door locks, etc.
A Holistic Strategy
To learn more about uses and Applications of Natural Language Processing in different domains we advise taking the following steps -- Discover more about Google Cloud Natural Language Solutions
- Know the Differences Between NLP, NLU, And NLG
