Natural Language Processing(NLP) is the latest technology used to enhance computers to comprehend the human’s natural language. It relates to the interaction between computers and human using the natural language. The concern of NLP is to read, decipher, and understand human languages in a technical manner which is of great value. Most techniques are used to decipher depending on machine learning. It is generally considered a difficult task to interpret the nature of human language.
There are primarily two components of NLP
Natural Language Understanding (NLU)
Natural Language Generation (NLG)
NLU is the mapping of natural language and representing it while NLG is producing meaningful sentences in natural language. While the natural language is rich in its crude form it is extremely difficult to form a structure or represent it in sentences.
NLP calls for applying algorithms to distinguish and retrieve the natural language rules that are the unstructured and converted into a known form that computers can understand. When the data in the form of text is provided, the computer implement algorithms to retrieve the exact meaning and assemble the essential data from them. Sometimes, the computer may fail to understand the meaning of a sentence provided leading to wrong results.
NLP is under several technological experiments and researches which is not wholly perfect. Syntax and semantic interpretation and analysis are two main techniques implemented in natural language processing. Semantic analysis is quite difficult to challenge for NLP. Some other difficulties include the abstract use of language which is extremely tricky and difficult for programs to understand. Some topics usually require an understanding of the words and phrases used. There are forms, context and the way they are being used. These are the challenges that are complex and complicated for NLP. It also faces a challenge when the way of the language used is constantly changing and the usage of language.
Major NLP researches are often broken down into sub forms and easier tasks. For instance, Google Translator is one of the few examples which can translate speech to desired speech directly by operating on spectrograms without the intermediate steps and of speech to text and text to speech. A few other examples like Amazon Web Services, Microsoft Azure, and Google Cloud offer natural language processing services of a different kind also with speech recognition and language translation services. These are some of the best generic NLP models, found in customized NLP.