What aspects can NLP be applied to?
In a previous article, we introduced “How to make investment decisions with knowledge graphs?”. In the industry, NLP (Natural Language Processing) is one of the technical difficulties of knowledge mapping.
JarvisPlus is an entrepreneurial project dedicated to providing quality community services. We are based on natural language processing technology (NLP) to improve the bot’s understanding and feedback on the language of customers or community participants, thereby gradually building emotional connection with community participants, which helps community managers maintain a good atmosphere, and promptly serve everyone.
In this article, we want to share with you what NLP can do and what aspects it will be applied in our lives. If you want to know about it, come and have a look!
The language model is undoubtedly the most important branch of research in natural language processing. The language model is a benchmark task that helps us measure our progress in understanding natural language. What’s more, language models are part of many other natural language tasks, especially those that involve generating text or estimating the probability of text.
In fact, we use language models every day. For example, the input method tool automatically generates the next word or phrase based on what you have previously entered to facilitate our input. As another example, when we use a search engine, the search engine automatically generates common search terms.
Take the example of iPhone we use every day, whether it is the voice input function when typing, or the powerful assistant Siri, when we use voice and machine to interact, there is support for speech recognition algorithms. From voice input to text recognized on the mobile phone, speech recognition completes the conversion from voice data to text data.
As an essential communication method for human beings, voice is an important part of many AI products, which is related to user experience. For example, when driving to navigate, we often use map software. The driver can interact with the navigation app only by voice, thus completely liberating the hands and ensuring driving safety.
Machine translation is the “gospel” of those of us who have not learned a second language well. We input text in a specific language (such as English), output the language we want (such as Chinese), and the input and output are semantically consistent.
A long time ago, machine translation was a collection of algorithms. For example, in 1954, the first machine translation system “Brain” translated Russian into English. If you are good at history, maybe you have found that it was at the time of the Cold War, and the Cold War led to the development of a machine translation system:) The translation system saved a dictionary of Russian to English, and then the language model combed the translated words into more reasonable sentences.
Today, machine translation basically uses an end-to-end training model that uses a combination of Encoder and Decoder to directly train the entire neural network model. We can even easily recognize the input of Multi-Language.
Question Answering/ Dialogue
Although the two tasks are put together here, the automatic Q&A and dialogue differ greatly in technical requirements. In general, automated questions and answers have clear answers, and automated conversations require more manual assessment.
Like the customer service bots mentioned in our previous article, they only have a simple automatic Q&A function. When the customer’s question triggers the default keyword of the bot, it will give a corresponding answer, but we all know that this setting can only meet part of our needs.
The JarvisPlus project has bots that have the function of automatic dialogue. Automatic dialogue are much more complex than question answering technically. This requires the computer to behave like a human in a conversation. If you are interested, you can click here to contact us and apply our dialogue bot.
In this field of research, there are many sub-branches. Some people are studying how to make the computer classify the speaker’s behavior (Dialogue act classification), some people try to track the state of the dialogue (Dialogue state tracking), and some people are doing researches on generative-based chatbot that generates a compelling response based on conversations.
Sentiment analysis/ Text Classification/ NLI
Emotional analysis is an area where the natural language processing technology is widely used currently. Usually, we use emotion analysis as a text classification problem to make qualitative judgments on the emotions of the text. The task needs to judge whether the user’s emotion is positive or negative according to the user’s comments.
This feature has great practical significance. The JarvisPlus bots also have this capability and is constantly iterating. Through emotional analysis, we can get a real-time understanding of the discussion and atmosphere of an online community. When we see a figure like the sentiment index, we can have a more accurate understanding and control over the community.
In addition, if you are a business, our bots can also forward relevant news stories to your online community and help you organize events to increase customer viscosity and community activity through continuous positive circulation. In the end, the effect of brand reputation expansion and profit maximization can be achieved.
That’s all for today. See you next time！
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Official Website: www.jarvisplus.com