The application of NLP technology in practical scenarios may not be “extremely extended”, but it is definitely not so far away from our daily life.
For example, chatbots can be roughly divided into “general-purpose” and “special domain” bots. The Microsoft Ice chatbot is a universal chatbot. Although it does have some lag, it does not prevent bored people from chatting with her all night long. Some domain-type chatbots, such as Amazon’s customer service and medical consultation apps, have really facilitated our lives and reduced labor costs for stores and hospitals.
NLP can be profitable. Take machine translation as an example: by learning a large number of professional vocabulary and translation materials in a certain field, a professional commercial translation system can provide better results, thus improving efficiency. This “commercial translation system” is not free.
It is not just turning NLP technology into a commodity on the shelf, with a price tag, that makes “sense”.
The search engine we use every day seems simple, but it needs various NLP technologies such as text segmentation, information extraction, and text classification to support.
Thanks to the recommendation algorithm, we can continuously read the content we are interested in through the recommended reading section on the news platforms as on various shopping platforms.
When we comment on a product on shopping platforms, the machine will analyze these comments, label them, and serve as the basis for others for reference. This process includes information mining, emotion analysis, semantic understanding…
In these applications, NLP technology has indirect commercial value. If the truly meaningful application is defined as “daily life consumer products” based on “NLP technology” and “large-scale application”, in terms of the existing market environment, products that meet this condition are indeed relatively rare.
Behind a small search box, there is the wisdom and efforts of countless people.
Whether the technology will be integrated into products and services ultimately depends on the market demand.
For example, when NLP is combined with speech recognition, speech synthesis and is equipped with certain hardware, an interactive interpreter translation machine can be achieved. While speaking a word, the machine can translate, so as to realize cross-language communication with foreigners anytime and anywhere.
After the smart speaker, everyone is trying this kind of “translation machine”. Only by using the product can we gain experience to improve or change direction, and the training data acquired along the way can also help us to better improve the technology.
NLP technology is the “driving force”. They are not sold like in packages, but they have made a significant impact on improving the quality of our lives and work efficiency.
After years of development, NLP is well suited for tasks related to lexical processing. Moreover, NLP is good at solving classification problems.
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