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Why is NLP important?

                 Communication through text or speech is a common method of interaction among humans. Now, computers are
                 equipped with a technology called Natural Language Processing, enabling them to understand, learn, process, and
                 manipulate human language.
                 Applications of Natural Language Processing

                 Natural Language Processing (NLP) is at the forefront of bridging the gap between human communication and
                 machine intelligence. By enabling computers to understand, interpret, and generate human language, NLP has
                 become a transformative force in modern technology. Its applications are far-reaching, impacting nearly every
                 industry and aspect of daily life.
                 Some real-life applications of NLP are as follows:
                    • Automatic Text Summarisation: Automatic Text Summarisation is the process of creating the most meaningful
                   and relevant summary of voluminous texts from multiple sources. Google News, Blogspot, Inshorts app and
                   many other apps are dealing with text summarisation tasks by using Machine learning algorithms that helps in
                   producing short and relevant data from the multiple sources, by identifying the important sections in a huge
                   textual dataset.
                                                                                  Document                 Summary
                   There are two different approaches of creating Automatic
                   text summarisation:
                   o  Extractive summarisation:  In  this  approach,  selected
                     text, phrases, sentences or sections are extracted from
                     the multiple sources and joined appropriately to form a
                     concise summary.
                   o  Abstractive  Summarisation:  In  this  approach,  the
                     summary is created by interpreting the text from multiple
                     sources  using  advanced  NLP  techniques.  This  new
                     summary may or may not have text, phrases or sentences from the original documents.

                                                                   •  Autogenerated  Captions:  NLP  is  used  to  produce
                                                                      descriptive  text  for  images,  videos  or  real-world
                                                                      contexts. They are widely used in areas such as social
                                                                      media and content creation. The captions are generated
                                                                      using speech-to-text and image-to-text features on a
                                                                      real time basis. For example, Captions are automatically
                                                                      generated for speech, in the language of the meeting,
                                                                      using Instagram’s or YouTube’s captioning tools.




                    • Language Translation:  It  refers  to  the  process  of
                   converting text or speech from one language to another
                   using NLP. This technology aids in learning new languages
                   and facilitates cross-cultural understanding. The process
                   of  language  translation  continues  to  evolve,  with
                   advancements in AI and NLP making it increasingly fast,
                   accurate, and accessible. For example, Google Translate
                   uses deep learning models to improve translation quality
                   over time.
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