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NATURAL LANGUAGE PROCESSING (NLP)

sush2609ghosh
Jul 21, 20215 min read

Introduction

Natural Language Processing or NLP (also known as Natural Language Technology) is a technology that is a branch of Artificial Intelligence-driven technology. It is concerned to give the computers the ability to read and understand the text and spoken words like human interactions. Natural Language Technology is the combination of computational linguistics with statistics, machine learning, and deep learning models (Picture 1). These technologies together enable the computer for the processing of human languages which are originally in the form of text or voice data input and understanding the full meaning. Now in all the social media, like Facebook, Twitter, Youtube, Instagram NLP is widely used as the users are growing rapidly.


Picture 1


History

The concept of Natural Language Processing is summarised by John Searle by the experiment Chinese Room experiment.


Importance of Natural Language Processing (NLP)

Natural Language Processing is important in the fields of businesses where verbal or written communications are widely used and it is used for the analysis of large volumes of text data like the comments made in social media, online reviews, reports, and many more.


As this data contains valuable insights, Natural Language Processing (Picture 2) helps in discovering what those insights are about. And what actually the text contains.

Picture 2


Natural Language Processing data in real-time, 24/7 without any fail, applying the same criteria so that it is ensured that the results we receive are cent percent accurate without any inconsistencies. As soon as Natural Language Technology recognizes what a piece of text is about, even things like sentiments, the concerning business can start to give priority and to organize their data in the way which suits their needs.


Working principle of Natural Language Technology

In Natural Language Technology, human languages are separated into different parts so that the grammatical structure of the sentences and the word meanings can be easily analyzed and understood (Picture 3). This process helps the machines to read and understand the speech or written texts like humans. Natural language technology analyses the data to determine its actual meaning by using different algorithms (of AI) for the reduction of human speech or text into a data model which consists of semantics and programmatic definitions. In general, it is either supervised or unsupervised learning of Machine Learning (ML).


Picture 3


Two concepts of Natural Language Technology are intent and entity recognition.

  1. Intent recognition is the process by which the sentiment of a user is identified and the objective is determined. This is the first and most important step of Natural Language Technology as it establishes the actual meaning of the text or voice input.

  2. Entity recognition is a specific type of Natural Language Technology that determines the identification of the entities in a message. It extracts the most important information from the input. The two types of entities are named entities and numeric entities. While the named entities are grouped into the categories of people companies locations et cetera the numeric entities recognize as numbers currencies and percentages (Picture 4).

Picture 4


A few fundamental Natural Language Technology pre-processing tasks which the scientists need to perform are mentioned below:-

  • Tokenization - It divides the text into smaller semantic units or single clauses.

  • Parts–of–speech-tagging - It marks the words as nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunction, etc.

  • Stemming and Lemmatization – Standardize the words by reducing them in their root forms.

  • Removal of stop words - Filtering out and removing some common words which do not contain any unique information.


Natural Language Processing Algorithm

As soon as the data has been pre-processed by AI algorithms, it moves to the next step of building algorithms and understanding so that it can easily interpret the language and perform the tasks as required.


The two main algorithms used to solve Natural Language Technology problems are:-

  1. Rule-Based Approach – This system relies only on hand-crafted grammatical rules that are required to be created by experts in linguistics or those who are knowledge engineers. This was the earliest approach of Natural Language Technologyalgorithms and it is still used today.

  2. Machine Learning Algorithm – It is based on the statistical data and methods and learning (supervised and unsupervised) to perform tasks after they are being fed. The greatest advantage of a machine learning algorithm is its ability to learn on its own ability. It is not required to define manual rules instead of which machines can learn from previous data (from supervised learning) to make predictions on their own (unsupervised learning) and hence allow more flexibility.


Natural Language Technology Examples

Natural Language Technology allows its users to perform a variety of tasks (Picture 5) like text classification, extraction of text, translation into machine language, machine translation, top modeling, and natural language generation.

Picture 5


  1. Text Classification - This is one of the most important tasks which consists of the assignment of categories to a text best on its content.

  2. Text Extraction – It is another example of Natural Language Technology consisting of pulling out specific pieces of data which are already present in the text. It summarises the text and finds the key information automatically.

  3. Machine Translation – This is used by the online translation tools for achieving human-level of accuracy in translating the speech and the text input into different languages.

  4. Top Modelling - It is similar to text classification. It finds relevant topics in a text by grouping them with similar words or expressions.

  5. Natural Language Generation - This analysis on structured data and use it as an input to create content automatically. It can be used in generating automated answers by writing emails and even books.


Applications of Natural Language Technology

Natural Language Technology is greatly used in businesses to understand unstructured data like emails, posts in social media reviews of the products online service, and customer support tickets and to gain valuable insights which enhance their processes in decision making (Picture 6). Companies use it to automate a routine task which reduces time and cost and ultimately becomes more efficient.


Picture 6


Automatic analysis of customer feedback

Analysis of customers’ feedback is very essential to know what the views of the customers about the product are. Cases in these data become difficult to process. Natural language Technology helps in converting data from online service product reviews for social media posts and gets insights for the improvement of your business.


Best Saas NLP Tools

  1. MonkeyLearn

  2. Google Cloud NLP

  3. IBM Watson

  4. Lexalytics

  5. Aylien

  6. Amazon Comprehend

  7. Clarabridge

  8. MeaningCloud


Conclusion

Natural Language Technology is one of the most important and advantageous fields within the area of Artificial Intelligence. It is already present in many applications which we use in our daily (Picture 7). Although it is a bit complicated, it is becoming more and more accessible to the users. Its future is vast and enormous opportunities are there.


Picture 7

 
 
 

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Internship

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An internship is a temporary job role offered to students for professional learning experience which offers meaningful, practical work related experience to students’ fields or career interests.

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Conclusion

 

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Thus whether the internship is paid or non-paid don’t worry about that. Your professional experience (essential for fresher job seeker) and growth will be count from the first day of internship.

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