Imagine a human talking to a device. This has been fiction in pop-culture for decades, which we now experience as a reality. Machines or devices are able to have this interaction with humans with the help of natural language processing. This language processing is the ability of a computer program to understand the human language either in the form of text or speech and is enabling language translations and voice-command-interactions between machines and humans. Natural language processing combines deep learning, artificial intelligence and speech recognition to deliver an almost lifelike experience. The Speech Recognition matches voice instructions with its vocabulary to identify the words and phrases before NLP helps the machine to understand the instructions to perform the action intended from the human request.
However, translation between languages is a much more difficult process to develop. For example, translating between English and French is more complicated than translating English into a computer language. It is not merely replacing English words with French words. It requires the development of phrase structure, which also includes the grammatical rules of the language. Therefore, the barriers of ambiguity, slang, improper grammar and multi-lingual culture are challenging to breakthrough.
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To solve these challenges, developers need to design deep learning-based language models. These language models are able to analyze the sequence of the phrases and differentiate between similar phrases to understand the user’s most probable intent. Training the model with users from diverse languages, slang, or a high level of communication skills makes the model much more accurate.
As businesses are transforming into data first enterprises, having access to consumer insights are becoming vital element to drive business strategies. Data solutions with NLP capabilities are helping enterprises to decode consumer opinion expressed on various social platforms and to deliver insights and predict attrition. Whether it is translating human language to machine language or translating between different human languages, NLP is critical to bridge the language gap between humans and the gap between humans and machines.
Organizations that are implementing device solutions are reporting that using natural language processing lets them deliver better experiences to their language diverse customers in their own native language as well as the option to include other languages that are relevant to the business. Chatbots, applications or a website UI that lets consumers interact in their local language is becoming essential to reach markets that are untapped because of the language barrier.
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