Approaches in Natural Language Processing

Natural Language Processing involves machines or robots to understand and execute the language that human speak. It is a connection between computers and human language. Natural Language is the computer understanding, execution, twiddle and creation of natural language. Natural language processing (NLP) is a way to translate between a computer and human languages.
The creation of NLP is daring because computers conventionally require humans to speak with them in a coded language that is brief, unambiguous and organised. Human speech is not always brief it is often ambiguous and semantic structure which can depend on many variables including slang, regional dialects and social context etc. In other words, NLP automates the translation process between computers and humans.
Natural Language refers to speech analysis in both audible speeches as well as of a text language. NLP system grabs meaning from an input of words (sentence, paragraphs, pages etc.) in the form of formal and organised results. Natural Language is a basic part of Artificial Intelligence. It is far more than just speech interpretation. There are various approaches for human language which includes:


The symbolic approach to natural language processing is based on human-developed regulations and lexicons. In other words, the foundation behind this approach is in generally approved regulations of speech within a specific language which is materialised and recorded by experts.


The statistical approach to natural language processing is based on observable and persistent examples of semantic phenomena. Models which are based on statistics identify persistent themes through mathematical interpretation of the large text. By recognising trends in huge samples of text the computer system can develop its own semantic rules that it will use to interpret future input variables and the development of language output.


The connectionist approach to natural language processing is a mixture of the symbolic and statistical approaches. This approach starts with generally approved rules of language and converts them to specific applications from input procured from statistical inference.

How Systems Interpret Language:

Morphological Level:

Morphemes are the smallest units of meaning within words and this level deals with morphemes in their role as the parts that makeup word.

Lexical Level:

This level of speech analysis examines how the parts of words (morphemes) combine to make words and how slight differences can dramatically change the meaning of the final word.

Syntactic Level:

This level aims at text at the sentence level. Syntax rotates around the plan that in most languages the sense of a sentence is dependent on word order and dependency.

Semantic Level:

Semantics focuses on how the context of words within a sentence helps determine the meaning of words on an individual level.

Discourse Level:

How sentences relate to one another. Sentence order and arrangement can affect the meaning of the sentences.

Pragmatic Level:

Bases meaning of words or sentences on situational awareness and world knowledge. Basically, what meaning is most likely and would make the most sense.

Ultimate Aim

The ultimate aim of natural language processing is for computers to achieve human-like comprehension of texts/languages. When this is attained, computer systems will be able to interpret, summarise, translate and generate accurate and natural human text and language.

Image Courtesy- Expert Systems


All about Natural Language Processing

The domain of research that point out the interactions between human languages and computers is called Natural Language Processing or NLP. It stands at the junction of computer science, artificial intelligence and computational linguistics.
NLP is a path for computers to inspect, understand and derive meaning from human language in a smart and useful manner. Through NLP, developers of it can collect and establish ideas to perform tasks such as automatic summarization, translation, entity recognition, sentiment/emotion analysis and speech recognition. It also has advanced features like correcting grammar, converting speech to text and automatically translates between two languages. NLP is generally used for text mining, machine translation and automated question answering.
A 2017 report on the natural language processing (NLP) market estimated that the total NLP software, hardware and services market share to be around $22.3 billion by 2025. The report also says that NLP software solutions influencing Artificial Intelligence will see a market growth from $136 million in 2016 to $5.4 billion by 2025.
We can see in the below mentioned figuretrends

Example Natural Language Processing Use Cases

NLP is based on machine learning algorithms. Rather than using hand coding commands, NLP can rely on machine learning to automatically learn these set of rules by interpreting a set of examples. Social media analysis is an example of NLP use.

Current Applications of Natural Language Processing

Customer Service

Some of the current virtual assistance solutions using NLP serve as intelligence enhancement. In such applications, a customer’s first request is checked by the artificial intelligence such as apps like Nina. E.g. a banking customer service system uses the AI to answer some basic transactional difficulties such as opening an account or to figure out the best loyal customer of the bank.


It also offers automotive virtual assistants connected to flagship cars OEM like BMW, JAGUAR, AUDI and others. One press release on Nuance’s partnership with BMW mentioned about Dragon Drive AI which enables drivers to access apps and services through voice commands, navigation, music, message, calendar, weather and social media.
It is possible to give a command to Artificial Intelligence to send a text message right from the car like, text Bella, “I will reach 10 minutes late at home” or “Get me directions to Dominos Pizza in Indore” etc.


NLP also provides solutions in healthcare domain. It includes clinical document improvement solutions. CDI is a process of improvising healthcare record of the patients to ensure good health of a patient, data quality etc. In this field AI allows physicians to write progress notes of patients, history of present illness and also plans or strategies need to be adopted for further actions. NLP provides real-time intelligence to physicians by automatically prompting them with clarifying questions while they are documenting.
There are many AI & NLP applications in the market. It is very important to choose the correct application that can resolve business problems with the help of technology and provide value.
Finally, businesses must have enough relevant data for learning algorithms for accurate outputs.

Future possibilities with NLP

  • Researchers are working on making AI more human-like, which is really a tough task. (E.g. making a conversational AI)
  • Expansion of existing AI technologies (E.g. extending automatic picture captioning to healthcare and other applications for clarification of image)