Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence that focuses on how to get computers to process and analyze large amounts of human natural language data. Common NLP challenges are speech recognition, natural language understanding, machine translation, and natural language generation.
Natural Language Processing (NLP) is a potential application of artificial intelligence. With the gradual maturity of AI technology in recent years, the application of NLP in various industries has also expanded. A study shows that in the five years from 2019 to 2024, the NLP market will further grow by 259% to $26.4 billion, and companies in different industries are creating value through NLP.
However, even though natural language processing technology has been applied in various links, at this stage, NLP cannot perfectly distinguish the subtle gaps in words, so a universal NLP architecture has not yet emerged. However, with the growth of computing power, breakthroughs in deep learning technology, and the further maturity of algorithm models, the application fields of NLP will be able to create value for enterprises in a wide range and depth.
What is Natural Language Processing (NLP)?
Natural language processing is a technology that allows machines to recognize, understand and use language through complex mathematical models and algorithms. Machine translation is a type of NLP application. When the searcher inputs the text that needs to be translated into the so-called NLP system, the algorithms and models behind it will process the processes of identification, understanding, and generation, and finally output the translated text. Targeting language information and giving computers the ability to understand human language is what NLP technology strives to achieve.
Early NLP technology was mainly based on statistical concepts to train the model. Algorithms would be used to read a large number of dictionary-like paragraphs of articles, and then the algorithm would calculate the probability of occurrence of words and sentences. However, with this method the system cannot consistently identify complex grammars and the words produced by such models are more rigid and disordered. However, with the breakthrough of deep learning and new algorithm models, new operating methods have been designed to better recognize and judge input, and then produce more accurate results.
The emergence of deep learning has changed past modes of NLP training, and the new algorithm model now most widely used by researchers is BERT (Bidirectional Encoder Representations from Transformers). This is a set of algorithms open-sourced by Google based on the Transformer architecture model.
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 that used in previous models. By forming better connections between content in the text, the context can be more comprehensively understood, which then helps the system more accurately generate text. Google introduced the BERT model last year to improve its 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 entered by the user 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. 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 the language model established by quantifying vocabulary.
Word embedding is the most common training method. The words themselves are marked with vectors of different dimensions. The words with more related meanings are closer to the vector distance, and vice versa. BERT 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 words but also uses a more natural way to check the upper and lower texts to achieve language recognition. The trained model is not only more general, and can better solve the difference of word meaning, which is 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 is the reverse of natural language understanding. The goal of the system is to extract data from the database and integrate it into output machine-readable data which then be used to generate natural language. The system must convert the data from a structure that only machines can understand, (binary machine language like 0101010101), into words that humans can understand. These tasks include processes like summarization, news automation, and machine translation.
Over the past few years, language generation has often used Recurrent Neural Networks (RNNs) to build neural language models, which trains the model to predict the probability of the next generated word in a way that takes into account the previous text. The training speed of the RNN algorithms is not only more efficient, but also the accuracy of word prediction in a two-way context is better. Now most of the machine learning models in the field of NLG are based on RNN.
The main research topics of natural language processing:
- Speech to Text / Text to Speech
- Part-of-Speech Tagging (POS Tagging)
- Natural Language Generation
- Topic Model / Text Categorization
- Information Retrieval
- Named Entity Recognition
- Information Extraction
- Question Answering
- Machine Translation
Five areas of application of NLP technology:
With the advancement of deep learning, the application field of NLP technology has become wider, and the adoption rate of NLP by enterprises has increased significantly. NLP technology can operate 24 hours a day, and its error rates are extremely low. As this technology becomes more mature, wider application of NLP will create more value for the market.
For enterprises, the value provided by NLP can be divided into three aspects, one is operational efficiency and cost reduction, another is customer journey and experience optimization, and finally, the business driven by NLP in various industry model. For example, sentiment analysis is an application of customer journey and experience optimization, and more and more startups are using this technology to develop new business models.
-
Chatbot:
In the past, 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. This not only increased labor costs, but also, these operators could not always handle the huge number of customers and provide the extensive information required. To give a favorable customer experience, a high level of training was required for customer service personnel.
This is why chatbots were gradually introduced. Chatbots not only provide instant services around the clock, but also provide more accurate product information and personalized services. Based on these two advantages, chatbots can better access the opinions and needs of consumers and generate more effective consumer feedback. Chatbots can help reduce customer service costs by 30% and have become a powerful business tool to enrich the consumer experience.
-
Emotion analysis:
Sentiment analysis models are models that recognize words or conversations that contain opinions or emotions. They establish rules to quantify the vocabulary and recognize 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. Traditional feedback models are often based on insufficient data or unreal feedback, or consumers themselves do not know their own purchasing motives, or truly understand their consumer behavior. This is where sentiment analysis models can provide great value. Consumers also express their thoughts on social platforms and forums. By effectively using this data, industries can have a deeper understanding of consumer insights and better understand customers. By understanding what customers like and dislike, businesses can improve your products, business models, and customer service. Sentiment of the messages can be divided into positive, neutral, and negative, and aspects of customer satisfaction can be automatically calculated from it. This kind of analysis will provide enterprises with a clearer direction for improvement.
-
Assistant:
As this technology becomes more mature, industry players can apply it to better understand the real feelings of users or consumers. Traditional feedback models are often based on insufficient data or unreal feedback, or consumers themselves do not know their own purchasing motives, or truly understand their consumer behavior. This is where sentiment analysis models can provide great value. Consumers also express their thoughts on social platforms and forums. By effectively using this data, industries can have a deeper understanding of consumer insights and better understand customers. By understanding what customers like and dislike, businesses can improve your products, business models, and customer service. Sentiment of the messages can be divided into positive, neutral, and negative, and aspects of customer satisfaction can be automatically calculated from it. This kind of analysis will provide enterprises with a clearer direction for improvement.
The popularity of IoT devices indicates that in the future there will be more devices developed that can be connected through written text and voice. This is sure to lead to more significant developments in process optimization in many business environments.
-
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 for copywriting by 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. AI can be used to generate faster more effective marketing texts, deliver ads or emails to customers in a more personal way, and better communicate with existing and potential customers.
-
File handling:
The accuracy of AI in reviewing confidentiality clauses has reached 94%, while the average accuracy rate of reviewing by experienced lawyers is 85%. In only 26 seconds, AI was able to complete the work that took lawyers 92 minutes to review. Not only does AI have advantages in document review, but it can also provide business value in other areas such as in document classification or for completing repetitive tasks such as comparing peers, or performing business analysis.
The accuracy of AI in reviewing confidentiality clauses has reached 94%, while the average accuracy rate of reviewing by experienced lawyers is 85%. In only 26 seconds, AI was able to complete the work that took lawyers 92 minutes to review. Not only does AI have advantages in document review, but it can also provide business value in other areas such as in document classification or for completing repetitive tasks such as comparing peers, or performing business analysis.
