Natural language processing is a technology that enables machines to recognize, understand and use our language through complex mathematical models and algorithms. As the NLP technology becomes more mature, the machine can work 24 hours a day and the error rate is extremely low, which will drive the wider application of NLP and create more value for the market.
What is Natural Language Processing?
Natural language processing is a technology that enables machines to recognize, understand and use our language through complex mathematical models and algorithms. Machine translation is a type of NLP application. We input the text to be translated into the so-called NLP system, and the algorithm and model behind it will process the processes of identification, understanding, and generation, and finally output the translated target language.
The early NLP technology was mainly based on statistical concepts to train the model, allowing the algorithm to read a large number of dictionary-like paragraphs of articles, and then let the algorithm calculate the probability of occurrence of words and sentences. However, this method cannot make the system well identify complex grammars, at the same time, the words produced by such a model are more rigid and disordered. However, with the breakthrough of deep learning and algorithm models, new training methods have been able to better deal with the problems mentioned above.
The emergence of deep learning has changed the operation mode of training NLP in the past, and the algorithm model most widely used by researchers is BERT. The full name of BERT is Bidirectional Encoder Representations from Transformers, which is Google is based on a set of algorithm models open sourced on the Transformer architecture.
The significance of BERT is that it can pre-train the algorithm, look at the words before and after in both directions, and then infer the complete context. This approach is different from the previous models, which can connect the context more comprehensively and effectively help the system in the text. understand and generate. Google introduced the BERT model last year to improve its own search engine. In a recently published evaluation, BERT not only improved the ability of the search engine algorithm to understand English, but also better defined the user's search intent.
Natural Language Understanding (NLU)
The purpose of Natural Language Understanding is to enable the system to read the information we input, so that it can understand the text, language and extract information to help the execution of downstream tasks such as text classification, grammatical analysis, and information search.
When performing NLU, the smallest unit of data is words, words form sentences, and small sentences continue to form large sentences and articles, which means that when using NLU for any task, its primary goal is to identify words. Like the sentence "I like to eat apples", the algorithm must first distinguish between different parts of speech, and then further understand the relationship between words. In fact, from a mathematical point of view, the composition of any vocabulary can be connected or marked with numbers, which can be the probability of vocabulary occurrence or a language model established by quantifying vocabulary.
And word embedding is the most common training method. The words themselves are marked with vectors of different dimensions. The more related words, the closer the vector distance, and vice versa, such as: The vector distance between the computer and the calculation will be closer, and the vector distance between the computer and the running is further away.
The BERT mentioned above is also trained based on the concept of word embedding. The difference is that BERT not only uses word vectors to judge the structure of sentences, but also uses a more natural way to check the upper and lower texts to achieve language recognition. The trained model not only It is more general and can better resolve the differences in the meaning of words. To give a simple example: "Mr. Wang flew to Tokyo". Here, Mr. Wang will not be misunderstood as a bird, flapping his arms and flying to Tokyo, but It was Mr. Wang who took the flight to Tokyo. This level of understanding is also why NLU has been able to do sentiment analysis and understand the intention behind the utterance very well.
Natural Language Generation (NLG)
Natural language generation (Natural Language Generation) is the opposite of natural language understanding (NLU), the goal of the system is to integrate, extract, and extract the data in the database to output these machine-readable data in the form of natural language. Simply put, it is to convert the data structure that only machines can understand, that is, machine language like 0101010101, into words that humans can understand to complete tasks such as text summarization, news automation, and machine translation.
In the past few years, language generation has often used Recurrent Neural Networks (RNNs) to build neural language models, training the model to predict the probability of the next generated word in a way that takes into account the previous text. However, in recent years, algorithm models based on Transformer such as Open AI's GPT-2, Microsoft's Turing-NLG, or Google's BERT have replaced the training method of RNN. The training speed of these algorithms is not only faster than that of RNN. Efficient, and the accuracy of two-way context word prediction is better, so that most of the machine learning models in the NLG field are based on Transformer.
What are The Applications of NLP?
With the advancement of deep learning, NLP technology has become more widely used, and one report pointed out that the adoption rate of NLP by enterprises has increased significantly. As the NLP technology becomes more mature, the machine can work 24 hours a day and the error rate is extremely low, which will drive the wider application of NLP and create more value for the market.
For enterprises, we can divide the value level provided by NLP into three aspects: one is operational efficiency and cost reduction, the other is customer journey and experience optimization, and finally, various industries are driven by NLP through NLP. business model. For example, sentiment analysis is an application of customer journey and experience optimization, but we are also seeing more and more startups using this technology to develop new business models.
Chatbot
In the past, in order to interact with consumers at any time, enterprises needed to hire special personnel to be on call in front of the phone or communication platform 24/7, which not only consumed labor costs, but also could not handle the huge number of customers and information well, and the training level of customer service personnel was even higher. It will affect the customer experience on the front line.
This is why chatbots are gradually entering the mainstream, not only because they can provide instant services around the clock, but also because they can provide more accurate product information and personalized services. Based on these two advantages, chatbots can better access consumers' opinions and needs, drive more effective consumer feedback, and become a powerful tool for enterprises to enrich consumer experience. According to a survey by Oracle, 80% of respondents will use chatbots to serve customers, and a data from Maruti Techlabs pointed out that chatbots can help reduce customer service costs by 30%.
Emotion Analysis
A sentiment analysis model is a way of mining words or discourse opinions, and rules are established to quantify the vocabulary, so as to know the emotion, opinion or intention behind the words.
As this technology becomes more mature, industry players can apply it to better understand the real feelings of users or consumers. After all, traditional feedback models are often based on insufficient data, unreal feedback, or consumers themselves do not know factors such as his purchasing motives, without truly understanding consumer insights. This is where the sentiment analysis model can provide great value. After all, the same consumers will also express their thoughts on social platforms and forums. Only by effectively using this data, the industry can have a deeper understanding of consumer insights to understand customers. what you like and what you hate to improve your product, business and customer service.
Kaggle has an interesting example. By analyzing the sentiment of the twitter messages of American Airlines, the customer sentiment is divided into positive, neutral and negative, and the factors of customer satisfaction are automatically calculated from it, such as: flight comfort, luggage, flight. This type of analysis will provide companies with a clearer direction for improvement.
Assistant
According to a report, intelligent assistants will maintain an annual growth rate of 34% in the next few years. Such rapid growth comes from the voice assistants on smart devices, such as: Siri and Alexa, etc., to help users deal with personal affairs or connect with intelligence home appliances, but with the advancement of NLP, more and more companies have begun to develop intelligent assistants to optimize work processes. That is to say, intelligent assistants are no longer only for individuals and families, but also have many application levels in various businesses of enterprises.
In the previous year, a technology company launched an enterprise voice platform tool that combines AI and NLU technology to assist enterprises in their meeting processes. The meeting participants only need to focus on the conversation, and the assistant will record the relevant matters in sync and organize the meeting insights. Such a model not only makes the direction of the team clearer, it also saves time for the team to improve the work efficiency of the rest of the business.
According to a survey by Loupventures, Google Assistant has been able to answer nearly 90% of the questions correctly, and with the popularity of IoT devices, this also means that in the future, more devices will be connected through written text and voice, in process optimization and There will also be more significant developments in the business environment.
Text Generation
Text generation is an NLG technology that has been used for a long time. AI is good at processing and applying large amounts of data in real time. Therefore, in the past, text generation was often used in the copywriting of media and advertising companies. News automation is a good example. Machines can continuously browse news (data) from different sources and write texts, so that the news quickly appears on the Internet and TV. Compared with traditional processes, AI text generation is faster, less expensive, and more objective.
JP Morgan partnered with an AI startup in 2016 to use AI to generate faster, more effective marketing texts to deliver ads or emails to customers in a more personal way, resulting in a 450% increase in ad click-through rates. Marketing messages also significantly improved credit card and loan business performance and better communication with existing and potential customers.
File Handling
In the previous year, a contract review platform company conducted a study. In the study, the accuracy of AI in reviewing confidentiality clauses has reached 94%, while the average accuracy rate of reviewing experienced lawyers is 85%, and the AI only spends time. 92 minutes of work for a lawyer to do in 26 seconds. Not only does AI have advantages in document review, but it can also provide business value in other areas such as document classification or repetitive tasks such as comparing peers, or performing further analysis.
In the financial industry, we also mentioned in the future of the insurance industry that the industry uses the NLP algorithm to complete the automatic claim settlement process, which greatly reduces the claim settlement time and enriches the customer experience. While reducing manual errors, the internal operation process is more efficient.
