Monday, January 30, 2023
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Natural Language Processing (NLP)

Natural Language Processing (NLP) is a complex intellectual activity that gives the computer ability to scan and understand human speech either; spoken (speech recognition) or written and relay the significant result. The process involves making computers perform useful tasks using natural human Language. 

Natural Language processing combines filter linguistics and computer science to decipher language structure and guidelines and to make models which can be comprehended and break down the significant details from a speech or a text.

  1. Speech

The machine can recognize speech through the speech recognition featurehence stimulating human-to-human conversations. Through this feature, the machine translates speech to text, conducts a text analysis, then natural language generation, and finally text to speech. Thisprocess makes it easy for a machine to identify, translate, and respond to the speech sound produced in human speech. Speech recognition sights to generate methodologies and technologies that enable the recognition, and the translation of spoken language into text using phonetic and linguistic information. A good example is the YouTube text recognition feature in the subtitles tab. The feature translates human speech into text. Speech recognition is also affiliated with speech to text. Once the computers recognize your words, it writes them down in form of a text.

Natural Language processingworks closely with speech recognition and text recognition engines to allow computers to input the information. Data analysts and machine learning experts use daily data transferred by internet users to mimic human linguistic behavior. Natural language understanding comprises two components, that is natural language understanding and natural generation language. Natural language generation involves mapping the given input in natural language and analyzing various aspects of the language. Natural language generation uses artificial intelligence programming to produce natural language output from a data set. Natural Language processing translates unstructured data into structured data to be analyzed with the natural language translation. Natural Language Understanding assists the computer to understand the phrase intent and is used to classify and filter entities. Natural Language Understanding is used with search technology to better respond to queries. In traditional natural language techniques, a question is pulled into a graph structure that deconstructs the sentence. However, newer natural language techniques use div learning to assist machines to understand better the use of language by looking at previous queries and users’ responses to the results. Once the machine understands the user’s meaning, natural language generation works towards the goal of teaching computers to turn structured data into a natural language that can be used to respond to the user in a conversation. Natural language generation enables machines and humans to communicate seamlessly.

The future of Natural language processing

Natural language processing points to two major leads, being:

  1. Large Transformer Models such as GPT-3 
  2. Significant advancement will be in the dialogue models where Google, Facebook, and other companies’ channel huge sums of money into research and development. Google, for example recently unveiled a demonstration of a conversational Artificial Intelligence system called LAMDA. This AI system can connect with humans on a diverse number of topics, unlike the modern chatbots that are trained for narrow conversations. If successful, the AI system would possibly disrupt the help desk and customer support to usher in entirely new categories of helpful applications. 

Since Natural language processing is driven by data, it faces data-related problems.

  1. Low resource language as there are thousands of languages spoken whereas a bigger fraction receives attention. 
  2. Dealing with large or multiple documents Is a challenge as the current models are mostly based on recurrent neural networks which cannot represent longer contexts well.

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