You may have heard the term “ Natural Language Processing ” before. However, I think that there are many people who know what natural language processing is and what it can do, but do not know the details.
Therefore, in this article, I will introduce what natural language processing is, what it can do from the mechanism of natural language processing, and practical examples.
Table of Contents
- Mechanism of natural language processing
- What you can do with natural language processing
- Parsing large amounts of text data
- Processing unstructured data
- Natural language processing with python
- Translation
- Dialogue system
- Sentiment analysis
- Natural language processing that BERT can do
- Transfer learning (Fine-Tuning)
- Create FAQ data
- 6 Use Cases of Natural Language Processing
- Search engine
- Automatic translation
- Smart speaker
- Interactive AI chatbot
- Voice recognition AI
- Kana character conversion prediction
- Future and Challenges of Natural Language Processing
Mechanism of natural language processing
First, I will explain how natural language processing works.
Natural language processing is a technique that allows computers to analyze “natural language,” the language we normally speak.
For computers, human language has a very ambiguous and complicated structure. Therefore, advanced technology is required to analyze natural language with a computer.
So how do we do this advanced analysis?
To put it simply, it is common to follow the flow of “collecting high-quality data, then extracting the necessary parts and deleting unnecessary data” .
What is necessary for this data collection is a machine-readable dictionary for computers to understand vocabulary, and a corpus, which is a set of documents that record and store language usage.
After these preparations are completed, we will proceed with the analysis. Analysis is carried out in four steps: morphological analysis , syntactic analysis, semantic analysis, and contextual analysis. After that, by structuring and extracting the necessary information from the text data obtained from the above process, it becomes ready for computer processing.
What you can do with natural language processing
So what exactly can natural language processing do?
I will introduce what you can do through natural language processing.
Parsing large amounts of text data
The first is that it can parse large amounts of text data.
This technique is used in text mining. Text mining is a method of extracting useful information from large amounts of text data.
Processing unstructured data
Unstructured data is data that does not have a fixed structure, such as images and audio.
Unstructured data cannot be databased, so it was not suitable for statistics and processing.
However, natural language processing has made it possible to read the intention of the question from the structure of the sentence and the surrounding context.
Natural language processing with python
Python is one of the most used programming languages in the world in fields such as machine learning , and it can also be used for natural language processing.
Here, we will introduce an example of natural language processing using python.
translation
First is the translation. Translation is the replacement of one natural language with another.
There are cases where the original meaning cannot be conveyed by simply translating the text directly. However, natural language processing can translate even long and complicated sentences in their original context.
Dialogue system
The dialog system also leverages natural language processing.
AI assistant services such as “Siri” and “Google Assistant” are representative examples of this dialogue system. Many people use these services in their daily lives.
Natural language processing is mainly used for the mechanism of “understanding human language, deriving answers to it, and making proposals”.
Sentiment analysis
Natural language processing can not only understand human speech and provide appropriate responses to it, but it can also analyze a person’s emotions from sentences and conversations.
This is being used in various situations, such as whether or not reviews for products are positive.
Natural language processing that BERT can do
BERT is a natural language processing model announced by Google on October 11, 2018.
As a feature, it is highly versatile, and the learning model pre-learns a large amount of sentence data obtained from Wikipedia, BooksCorpus, etc., and can be applied to sentence understanding and sentiment analysis.
Transfer learning (Fine-Tuning)
BERT differs from conventional natural language processing models in that it can perform transfer learning (fine-tuning).
Transfer learning is a pre-learning model BERT that can be adapted to an existing task execution model, making it possible to improve the accuracy of the model.
A BERT transfer-learned model works by simply learning a small amount of additional data, so there is no need to build a model from scratch.
Create FAQ data
The first example of using BERT in Japan and commercializing it is “sAI FAQ builder”, a FAQ data creation service by Sciseed.
FQA means “Frequently Asked Question”.
“sAI FAQ builder” presents tags that are likely to be related to the user’s question by AI.
It is a service that allows you to reach the answer to your desired question by intuitively selecting tags that may be related to your question from the presented tags.
6 Use Cases of Natural Language Processing
Next, we will introduce the use cases of natural language processing.
Search engine
Natural language processing is used in various places, but a typical example is search engines.
In 2019, Google adopted BERT, the latest natural language processing model, for its search engine.
Until then, natural language processing was able to read words, but it was not possible to determine the content from the context. However, by using BERT, it is now possible to determine what users want to search from the context, and search engines can more precisely respond to user needs.
Automatic translation
I think many people have used automatic translation at least once. Just by entering Japanese, you can automatically translate it into various languages.
Today, it is possible to accurately translate not only text but also voice, and it is often used for conversations between people who do not understand the language.
Smart speaker
Smart speakers are gradually permeating our lives, such as “Alexa” and “Siri”.
The smart speaker recognizes voice, accurately interprets natural language, and performs operations as instructed.
Natural language processing technology can infer the app the user wants from the context. Therefore, if you ask the smart speaker the time, AI will search the clock application to obtain the time information, and it will be possible to perform natural language processing and answer in human words.
Interactive AI chatbot
A chatbot accurately understands the context and meaning of the sentences you type in, and converts the best answers into sentences.
Voice recognition AI
Speech recognition AI is also related to natural language processing. Only the human voice is extracted from the recorded data by voice recognition and transcribed into text in context, but AI cannot understand the meaning of human words by itself. Therefore, we then understand their meaning and context through natural language processing.
Kana character conversion prediction
Natural language processing technology is also used for predictive conversion on smartphones and personal computers.
In Japan, just by typing hiragana, you can convert it to the appropriate kanji, and predict and present the sentences that follow.
Future and Challenges of Natural Language Processing
What did you think.
In this article, we looked at what can be done from the mechanism of natural language processing and examples of its use.
Nowadays, the use of natural language processing, such as smart speakers and automatic translation, is increasing.
And natural language processing technology has made it possible to do many things that were not possible in the past. Also, with the development of new models in natural language processing, we will be able to do more things in the future.
