Natural Language-Processing: Applications & Techniques


Natural Language Processing: Applications & Techniques

Natural Language-Processing is a machine designed to understand human language through machines. In other words, it is defined as the automatic manipulation of natural language by software. It is not easy to teach machines to understand how humans interact with each other. However, certain software has made this task easier.

Text: Natural Language Processing

Not only by texts, but we can also make use of natural language processing in different ways, such as:

  • Signs
  • SMS
  • Web pages
  • Menus
  • Email
Natural Language Processing: Applications & Techniques
Natural Language Processing: Applications & Techniques

A Speech: Natural Language-Processing

More than just writing, we communicate with each other by speaking. It can be easy to learn to speak than to write something. Text and voice are two ways through which humans interact with each other. Therefore, just like the different types of data, there are some techniques or methods to understand the importance of natural language.

Interaction Between Human And A Machine

A typical interaction between humans and machines by using NLP goes as follows:

Step-1: Human talks to the computer

Step-2: The machine needs to capture the audio of what human talked about

Step-3: Audio conversion will take place in the machine

Step-4: Processing of the data

Step-5: Data to the audio conversion will take place

Step-6: Then, finally, the machine will respond to the human by just playing the audio data.

Applications Of Natural Language-Processing

Take a look at the applications of natural language processing:

  • Google translate; language translation applications
  • Word processors that employ NLP to check grammatical texts.
  • Interactive Voice Responses used to respond to the request of the user.
  • OK, Google, Alexa, and Siri are some of the assistant applications.

In short, natural-language-processing is a challenging aspect of computer science. The information that is passing to the machine is not an easy task to understand. There are some rules to give information that can be more high leveled. Moreover, other rules can be low-leveled.

Moreover, for the machine, it may be difficult to understand the language and the concept to deliver the message. However, humans are masters in human language; the characteristics of the NLP are difficult for the implementation.

Therefore, NLP needs to apply some of the algorithms to find natural language rules. The rules are that it can convert into a form of that computer language.

When the information passes, the computer needs to use its algorithms to get the meaning and need to collect the essential data. Besides, it can sometimes fail to understand the whole sentence meaning.

Natural Language Processing: Applications & Techniques
Natural Language Processing: Applications & Techniques

Techniques Used In Natural Language-Processing

Two main methods can be used to complete NLP tasks. They are:

Syntax:

In short, it refers to the forming of words correctly to make a meaningful sentence. It evaluates the meaning of the sentence by reforming it in a grammatically correct form. It can evaluate how natural languages will align with the rules of grammar. The machine algorithms apply grammatical rules to bring out the meaning of the text. Some of the techniques that are used are:

  • Lemmatization
  • Morphological segmentation
  • Word segmentation
  • Parts-of-speech
  • Parsing
  • Stemming
  • Sentence breaking

Semantics:

Moreover, It refers to the purpose that can convey by the information data or the text. It is one of the challenging aspects of NLP that is not solved yet. Thus, it applies machine algorithms to understand the words meaning and forming a sentence. Some of the techniques in the semantic analysis are:

  • Named entity recognition
  • Word sense disambiguation
  • Natural language generation
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