Main Applications in Natural Language Processing Field(NLP) — 1

3 min readDec 15, 2020

Computer vision, voice interaction, and natural language processing cover almost all the main application scenarios of artificial intelligence.

Today we’d like to talk about the main applications in the Natural Language Processing field. Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language. It is about how to program computers to process and analyze large amounts of natural language data.

Common Application Types in NLP Field

Industries such as customer service, public opinion, medical treatment, and education are taking advantage of text annotation. The common application types are machine translation, emotional analysis, chatbots (Question Answering System), Text classification.

Machine Translation

Everyone knows what translation is — we translate information from one language to another. When it comes to machine translation, we have to deal with “how”. The idea behind is simple — developing computer algorithms to make automatic translation possible. Perhaps the most famous one is Google translate.

Google translation is based on SMT(Statistical Machine Translation). It’s not a word-to-word job. Google collects text as much as possible and then processes it in the right way. It’s very similar to human beings in childhood as we gave meaning to words while abstracting and inferring.

Considering the ambiguity and flexibility of human language, machine translation is challenging. In the process of cognition, human beings will interpret or understand the language, and translate it at multiple layers. However, machine processes focus on data, language form, and structure. It is far away from understanding the meaning of language deeply.

Emotional Analysis

Sentiment analysis is an interesting NLP application to measure people’s opinion tendency, as the traditional public opinion survey has long been outdated. Even those who are eager to support a brand or political candidate are not always willing to take the time to fill out the questionnaire.

Sentiment analysis helps to check whether customers are satisfied with goods or services. Nowadays, people are more willing to share their views on social networks. Searching for negative text and identifying major complaints can significantly help marketing managers change concepts, improve products and advertisements, and reduce dissatisfaction. In turn, clear positive comments will increase ratings and demand.

Emotions can be divided into 3 categories, 11 categories, and 25 subcategories.

For example:

Congratulations on having passed the examination.

Belongs to which general category:

Positive
Negative
Neutral

Chatbot — Popular Application in Customer Service

The first chat robot appeared in the 1960s. After decades of development, NLP has become the foundation for creating chatbots. Although such systems are not perfect yet, they can handle standard tasks easily. Chatbots can run on many distributions, including the Internet, apps, and message platform.

In customer service, the annotation requirement is mainly concentrated on the Scenario Recognition and Response Recognition, taking one well-known intelligent service robot as an example, while interacting with the chatbot, the user is led to the corresponding scenario according to the content, and then chatbot let the user choose more subdivided response model. According to specific problems, the chatbot gives the corresponding answer. The whole process is the same as the sand gets filtered through the funnel.

For example:

What is the type of smartphone?

The question above belongs to which scenario:

Pre-sales
After-sales
Logistic
Delivery

In the early stage of establishing this response system, it is necessary to classify a large volume of content and tag the corresponding question into the corresponding model.

Text Classification

Text categorization uses machines to label text sets automatically according to a certain standard. By using NLP technology, text can be analyzed, and then assigned to a certain category according to its content.

There are mainly two ways of tag: online platform and offline table tag( like excel), according to the enterprise’s requirement.

Conclusion

The nature of human natural language makes some NLP tasks difficult — not all principles can be standardized, and some scenarios are very abstract. For example, irony and implicitness have not been solved effectively. NLP is still struggling with the inherent complexity of language elements. However, we don’t need to seek perfection from the beginning.

End

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source:https://zhuanlan.zhihu.com/p/100985227

Relevant articles:

1 Chatbot — One of the Most Popular Applications in the NLP Field

2 Main Applications in Natural Language Processing Field(NLP) — 2

3 Main Applications in Natural Language Processing Field(NLP) — 3