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NATURAL LANGUAGE PROCESSING (NLP)

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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

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.

An internship offers a student opportunity for career exploration, increase in experience, development in career front and learning new skills.

 

Benefits of Doing Internship

Doing an internship is very much beneficial for a student’s career front.

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In recent times, job listing often requires minimum job experience. If a student entering a workforce for the first time, he/she may not have such experience. An internship is a perfect way to fill up that gap. A valuable exposure to a company will give a first hand experience where a student can take part in meetings and perform assigned works.

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In the scientific fields, an internship may offer a student to assist with research in a laboratory. A student can find out his/her skills that had been learnt in the academic years in practical settings and can contribute to the research. Many permanent research jobs require such kind of post education training. Moreover, this kind of internship also helps in deciding what kind of laboratory is preferable and favourable for a student.

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An internship always enables exposure to people of various departments and jobs. From helping a senior management to sit for meetings, internship offers a student to observe daily functions in an office. Thus, it helps a student to decide what kind of job would be preferable.

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The most valuable mentor relationship begins with a personal connection which enhances the experience of both mentor and mentee. Being an intern may allow to meet a potential mentor naturally and develop a good relationship that actually offers proper guidance in career path.

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Internship is a practical way for the expansion of job network. The professionals met during internship might turn into most valuable connection that will show interest, enthusiasm and willingness for recruitments.

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The valuable job experiences enable to fill the resume with honesty and specificity. Not only the experiences of the duties and projects of the internship are added, but also the objective of seeking a permanent position can be more clearly described.

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The supervisors or mentors of an internship can be valuable references for an intern while pursuing a full time job. Pro-activeness, Positive mind and hard work will pay for open positions.

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Positive experiences acquired from an internship might help in getting hired for a permanent position in a company.

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Internship acts as preliminary employment training and HR personnel or the manager can observe an intern in various situations to determine if the intern may be a valuable addition to the team.

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Taking on an internship helps an intern to learn about the work environment. Moreover, one can have a much clearer idea of his/her strengths, weaknesses, likes and dislikes. Most importantly, an intern can have hands-on experience which gives far more confidence when it comes to job seeking and interviews.

Conclusion

 

To sum up, internships always play a very important role in shaping one’s career. They give a real exposure to working environments and help to develop the necessary skills required to stand out in a saturated job market.

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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