Natural language processing refers to technology for processing ambiguous information, which has been difficult for computers to date. In this article, we will explain in an easy-to-understand manner the detailed meaning and mechanism of natural language processing, as well as examples of systems used with AI.
table of contents
What is natural language
What is Natural Language Processing (NLP) with AI?
Mechanism of natural language processing
What you can do with natural language processing and examples of its use
summary
What is natural language
“Natural language” refers to natural language such as English, Chinese, and Japanese that humans use when speaking and writing, as the word suggests. Natural language is characterized by ambiguous expressions. For example, in everyday conversation, you may ask the other person, “What does that mean?” or “Is this correct?”
On the other hand, the opposite of natural language is the programming language, which is the language used to create programs to run computers. There is no ambiguity in programming languages because their purpose is to give instructions to computers. It is necessary to set rules for numbers and symbols, and to always have a constant interpretation of instructions.
What is Natural Language Processing (NLP) with AI?
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“Natural language processing” is a processing method for computers to recognize and process ambiguous natural language and handle it practically. Natural language processing is called “Natural Language Processing” in English, and is also abbreviated as “NLP”.
Inherently, computers are not good at processing ambiguous natural language elements. However, by using natural language processing mechanisms, it is possible to process large amounts of ambiguous data, such as the content of human conversations, and utilize it in system development and application development.
Mechanism of natural language processing
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From here, we will explain the mechanism and flow of natural language processing, divided into the following six flows.
machine readable catalog
corpus
Morphological analysis
Parsing
semantic analysis
contextual analysis
machine readable catalog
A machine-readable catalog (MAchine-Readable Catalog MARC) is a dictionary or catalog that is necessary for computers to understand language. It is an element that should be prepared in advance for natural language processing, and has the implication of converting human words so that computers can read them.
corpus
A corpus is a collection of recorded usages of words. It has the role of identifying and tagging verbs and adjectives, and is sometimes called “language complete collection” when expressed in Japanese. A corpus, like a machine-readable catalog, is positioned as a preliminary preparation for the process of natural language processing.
Morphological analysis
From here, we will explain the actual process of performing natural language processing. A morpheme is the smallest meaningful linguistic unit in a natural language sentence. As a concrete example, let’s consider the sentence “Woman with a big red bag”.
This sentence can be broken down as follows.
big: adjective
red: adjective
bag: noun
を: Particle
have: verb
ta: auxiliary verb
female: noun
Morphological analysis refers to dividing sentences in this way.
Parsing
Syntactic analysis is the process of analyzing the relationship between words divided by the morphological analysis described above.
Consider the earlier example of the woman with the big red bag. In this sentence, if humans think with common sense, it means “the bag is red and big”, but there is a possibility that the computer will mistakenly recognize “the woman is red and big”. By organizing the word connections by syntactic analysis, the computer recognizes that the words “big” and “red” are related to “bag”.
semantic analysis
Semantic analysis is the process of analyzing the meaning of sentences based on the information processed by syntactic analysis.
In the example so far, it means that the computer correctly understands the meaning of the sentence “Woman with a big red bag”. In semantic analysis, it is possible for a computer to make judgments such as “a sentence seems to be correct” or “a sentence that is not common sense” by thinking as follows.
High relevance between “bag” and “large”
“Bag” and “red” are highly related
Low relevance between “red” and “female”
Such judgments allow us to parse out the true meaning of “big red bag” rather than the erroneous meaning “big red woman”.
contextual analysis
Contextual analysis is a term that refers to the process of further analyzing the results of semantic analysis. Specifically, it performs advanced processing such as understanding context across multiple sentences and revealing omitted words. Through contextual analysis, it is finally possible to prevent differences in detailed recognition of context.
What you can do with natural language processing and examples of its use
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From here, we will explain what is possible with the use of natural language processing and actual use cases.
machine translation
Machine translation using natural language processing is being developed by various companies such as Google.
Among them, “DeepL translation” is a famous machine translation service that translates with high accuracy. DeepL translation is provided by DeepL, a company that develops artificial intelligence systems using deep learning.
When it became compatible with Japanese and Chinese in March 2020, the high accuracy of the sentences after translation became a big topic on the Internet. In addition, not only individuals who need translation, but also various media around the world that handle IT-related information, have taken up the accuracy of DeepL translation.
Interactive system/chatbot
Natural language processing is also used in dialogue systems installed in terminals such as “Google Assistant” and “Siri”. In such an interactive system, the computer understands human language, which is a natural language, and interacts with the user, allowing it to solve a problem or perform a desired operation.
Chatbots (interactive AI), which are often used on corporate websites, are also systems that utilize natural language processing. Resolving users’ questions through chat will help reduce the burden of inquiry work and improve efficiency. In addition, there are cases where chatbots have been introduced for internal use instead of external use, and have improved work efficiency, such as answering questions from employees.
prediction conversion
When you type a character on an electronic device such as a smartphone, the candidate word you want to input will be displayed in a converted state. , such a function is called a predictive conversion function. Predictive conversion is displayed based on input history, etc., and it is common to use natural language processing technology to display candidates.
In recent years, it is possible to display predictive conversion according to the context even for long sentences. The predictive conversion function is used in systems such as “Microsoft IME” and “Google Japanese Input”.
AI search system
The AI search system is a function that facilitates user searches. The advantage is that you can easily find the information you need by simply entering a small amount of information.
As an introduction example of an AI search system, the search system “Knowledge Explorer” that has been introduced to many companies is common. Knowledge Explorer provides functions such as searching the database for materials with a theme similar to the document being created.
It is possible to solve the problem of not being able to find out from the huge amount of accumulated databases and not being able to fully utilize them. Such a mechanism will increase the possibility of finding the desired data even if the information provided by the user is small and vague.
text mining
Text mining is a technology for extracting necessary information from text data. It is often used in the field of marketing, such as picking up customer voices from comments on questionnaires and SNS, and analyzing their needs.
It takes a lot of time and effort to extract the necessary information from a list of many sentences. By performing text mining using natural language processing, important information can be extracted efficiently. Specifically, it involves extracting the information necessary for picking up customer feedback from unstructured information, such as sentences written in free-response sections of questionnaires.
This method is widely applied in call centers and medical sites, and is used to obtain more useful information from customers and patients.
Utilization of big data
Natural language processing is also used in the utilization of big data. For example, a company that develops apparel brands must handle huge amounts of data such as sales and purchase histories for each store. There are many cases where the amount of data is so large that it cannot be used effectively for strategic planning.
In recent years, the number of services that can easily process and process big data is increasing. One example is “SOFIT Super REALISM” provided by Nippon Software Development. By having a structured understanding of data on customer purchase histories, sales, etc., in the future, it will be possible to come up with more accurate data-based strategies.
summary
Natural language learning is used in various situations such as information retrieval, predictive conversion, and dialogue systems.
By using natural language processing technology, it is possible to process ambiguous information that computers were not good at until now.
By increasing the amount of work that can be done with computers, it will lead to more efficient work and a solution to the shortage of human resources.
By deepening your understanding of natural language processing and making it a habit to think about it in relation to your work, let’s use it when considering its introduction.
Evolution of natural language processing (NLP) with growing expectations for marketing utilization of artificial intelligence (AI)
Now that artificial intelligence ( AI ) has come to be used in various business areas, the wave is certainly spreading to the area of marketing. AI can perform advanced tasks that humans have traditionally performed in the marketing field, such as consumer sentiment analysis using image recognition, hypotheses based on deep learning techniques, and advertising design and copy creation. level has evolved. In particular, there are great expectations for future accuracy improvements in natural language processing ( NLP ), in which AI analyzes large amounts of text data. In this article, I’d like to highlight a use case for NLP that is transforming many marketing efforts.
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Support marketing decision-making by analyzing consumer psychology and behavioral principles
Support marketing decision-making by analyzing consumer psychology and behavioral principles
Marketing that relies on human labor has already reached its limits. When it comes to marketing campaigns that generate great results, it’s no longer enough to simply reach out to your target audience with your offer and wait for a response. Personalized, data-driven marketing must deliver advanced value propositions and reach new audiences to deliver real results that support sustainable business growth.
Intelligent automation tools and AI technologies are essential to achieving this with small teams with limited resources. Among them, natural language processing (NLP) is an important technology that has evolved together with AI for a long time. The Internet space, which has already become a part of our lives, is full of text information and voice information of various granularities, all of which are important assets in planning marketing strategies. NLP uses AI to analyze the latent meanings of words written and spoken by humans, revealing the psychology and behavioral principles of consumers that are difficult to see from words alone .
In the following, we will introduce typical NLP utilization that supports marketing decision-making by quantitatively grasping “when”, “for whom”, and “what” to propose in the ever-changing market and marketing environment. I would like to take a look at the use case.
Extraction of common interests and topics of the audience
Extracting common interests and topics for your audience is an area where NLP can be very effective. For example, by analyzing what kind of opinions the audience is sending on SNS and e-commerce sites, it is possible to identify common interests and make optimal proposals to the target audience .
Audience ratings of competing products and services are also difficult to obtain unless companies disclose the information. For that reason, NLP can bring out high value from SNS, which is full of various word-of-mouth information.
Keyword detection for SEO
In keyword detection for SEO measures, by generating a keyword list of high interest from the huge amount of text data on the Internet and text data generated in communication with existing customers and matching it with your company’s SEO keywords, It will be used for planning and improving new measures . For example, if you analyze your customer support emails and find that they are asking a lot of questions about a particular feature, you can update your SEO keyword list accordingly to improve click-through rates and drive more traffic .
Gain advanced insights in less time from sentiment analysis
Sentiment analysis is a marketing technique that reveals whether consumers have positive or negative feelings about a question through surveys. Marketers can gain a lot of insight from here, but one bottleneck is that it takes a lot of time to collect and analyze open-ended questionnaires.
In addition, the expressions and granularity of the words used in open-ended responses vary from respondent to respondent, and analysis also tends to depend on the subjective judgment of the person in charge. By using NLP to address these issues, it will be possible to obtain highly accurate insights based on clear criteria in a short period of time .
Improve lead quality with chatbot data analytics
Chatbots are rapidly gaining popularity as a way to communicate with potential or existing customers without human intervention . By analyzing the text information aggregated here with NLP, the priority of questions and prospective customers
You can reveal the quality (the quality of the lead).
Current chatbots can only handle tasks that have been learned and are limited to a certain range. The analysis results are highly valuable because the clarification of trends will lead to the preparation of many response patterns that lead to customer satisfaction and the identification of product improvement points.
Advanced contextual understanding that also enables analysis of voice searches
The above are typical use cases of NLP using AI analysis of text data. There are many other use cases.
NLP technology is also applied to listening devices such as Siri and Alexa, which are rapidly spreading, and voice search systems. In the future, NLP will undoubtedly bring even greater results in the field of marketing as it evolves to support contextual understanding of spoken language, which is even more advanced than Internet searches using words.
From the perspective of research on a “general-purpose language model” that supports the evolution of NLP, the general-purpose language model “BERT”, which was released by Google in 2018 and is now used in Google searches, can be used for complex word combinations and sentences. responsive and able to return the search results users want with unprecedented precision.
In addition, “GPT-2 (Generative Pre-trained Transformer-2)” announced in 2019 by the U.S. non-profit organization “OpenAI” uses AI to learn 10 million pages of web pages to transform sentences. It shocked the world as an epoch-making, automatically generated general-purpose language model. Following this, OpenAI announced “ GPT-3 ” in 2020, which is said to achieve accuracy that is indistinguishable from human-written sentences by learning a huge amount of text data.
These evolutions show the great potential of NLP to push the current chatbot-like system, which can only perform tasks based on rules and scenarios, to a higher level. Looking at uses other than marketing, in the current business environment where face-to-face communication is restricted due to the corona crisis with no exit in sight, much communication is done on platforms such as in-house SNS. Again, a huge amount of text data is accumulated here.
By analyzing this data, it is possible to promote unprecedented business transformation, so NLP is truly a technology of the future that brings innovation not only in the marketing field but also in all business fields. The future evolution of NLP, which reads the meaning contained in the words spoken by people everywhere and predicts the future needs, will attract a great deal of attention.