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With the generative AI boom that has arrived in waves since late 2022, AI that handles natural language, led by "ChatGPT" and followed by "Bard" and "Claude," has become part of our daily lives. However, few people likely understand how natural language is actually understood and generated by AI.
In this article, we explain application examples of Natural Language Processing (NLP) and how to create data for NLP models. In particular, we will explain in detail how "annotation," the process of creating data, is performed.
If you want to understand the mechanisms of NLP and examples of its use in business, please use this as a reference.
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【Table of Contents】 |
1. What is Natural Language Processing?
Natural Language Processing (NLP) is a technology where computers process natural languages like Japanese and English that we use daily. "ChatGPT," which is said to be the trigger for generative AI, utilizes Large Language Models (LLM) and possesses the ability to understand human language and respond naturally.
NLP AI is a technology that can be effectively utilized on its own, but by combining it with image generation AI or voice analysis AI, the creation of new value is expected in corporate customer support, market analysis, and product development. For example, ChatGPT can respond instantly to inquiries from customers and provide appropriate answers.
Furthermore, integration with image generation AI makes it possible to generate direct visual content from text-based information, realizing innovative expressions in advertisements and presentations, for example.
Why Language Annotation is Necessary in Natural Language Processing
Language annotation is a crucial process in NLP. When using supervised learning, it is necessary to attach "correct answer" labels to text data in order to teach the AI model the right answers. This is the role of language annotation.
However, language annotation is not sufficient by simply labeling words. To fully understand natural language, elements such as context, emotion, and intent must also be taken into account.
For example, the Japanese word "Sumimasen" carries multiple meanings such as apology, gratitude, calling out, blame, or greeting depending on the situation. Therefore, in language annotation, it is necessary to provide complex information including these contexts and nuances to accurately convey the meaning of the word to the model.
Through language annotation, AI models learn not just the meanings of words, but their usage and context, realizing more accurate and sophisticated natural language understanding.
2. Types of Language Annotation
To understand natural language, computers must accurately read intents, contexts, and emotions in addition to dictionary definitions of words. To read these, the following types of language annotation are performed:
- Semantic Annotation
- Entity Annotation
- Sentiment Annotation
- Intent Annotation
- Ontology Construction
- Phrase Chunking
Each of these is explained below.
- Semantic Annotation
In semantic annotation, specific meanings are assigned to each word and relationships between words are identified. By having the AI understand word meanings and relevance, it becomes able to correctly understand sentences.
For example, the word "butterfly" refers to an insect in a biological context, but may be used as a symbol of beauty or change in an artistic context, or refer to a woman in word combinations like "night butterfly" (Yoru no chou).
Semantic annotation takes the context of the entire text into account. This allows it to understand that the same word can have different meanings in different contexts.
Furthermore, part of semantic annotation involves understanding what the author of the text is trying to convey and what emotions or intents they have. For example, an expression like "Wonderful!" can mean sarcasm or criticism depending on the context.
Semantic annotation enables AI to understand human language more deeply and perform more human-like responses and analysis based on that understanding.
- Entity Annotation (Named Entity Recognition)
Entity annotation is the task of identifying specific entities within a sentence and categorizing them as names of people, places, or companies.
For example, in the sentence "Apple announced a new product," it is important to identify "Apple" as a company name. This allows the AI to correctly understand the contextual meaning of the word "Apple" and extract appropriate information.
Through entity annotation, the AI can recognize the category of each word, enabling it to accurately read the intent of the sentence. Performing entity annotation reduces the possibility of the AI making incorrect interpretations.
- Sentiment Annotation
Sentiment annotation is the task of labeling the emotions contained within text. Generally, emotions are judged across three levels:
- Positive
- Neutral
- Negative
For example, when analyzing customer feedback, it is possible to capture those emotional nuances.
By annotating emotions and training the model, it becomes possible to judge what kind of situation the speaker or writer is in. Usage tendencies of words according to emotions are also learned, enabling finer sentiment analysis. It also utilizes the characteristic that word tendencies differ depending on the emotion held.
- Intent Annotation
In intent annotation, the intent or purpose behind the text is labeled. Even if the same words are used, the intent of the entire sentence often differs.
For example, customer support chatbots rely on intent annotation to generate appropriate answers based on customer questions and requests.
Since the words and grammar used differ by intent, annotating those parts allows AI models to extract the intent. This makes it possible to accurately capture the emotional tone and intent of the text, enabling more natural and human-like dialogue and analysis.
Ontology Construction
Ontology construction refers to creating relationships between words. Examples of relevance include "is-a," "part-of," and "attribute-of."
| Relationship | Meaning | Example |
| is-a | Something is a type of something else | A sparrow is a bird (Sparrow is-a Bird) |
| part-of | Something is a part of something else | A wheel is a part of a car (Wheel part-of Car) |
| attribute-of | Something has a specific attribute | This car is red (Red attribute-of Car) |
If words within a sentence have some kind of relationship, they are linked. By constructing an ontology, the model can grasp the relationships between words, allowing it to understand the meaning of the text more deeply.
Phrase Chunking
Phrase chunking is the task of labeling parts of speech such as nouns and adjectives. If parts of speech are misinterpreted, the meaning of the entire sentence can fail to make sense.
For example, in the sentence "A large dog is running," it is important to distinguish "large" as an adjective and "dog" as a noun.
By performing phrase chunking, AI can correctly judge parts of speech and improve its understanding of sentences.
3. Is Language Annotation Possible with Automatic Tools?
To build a model capable of natural language processing, language annotation is often required. However, annotation is an extremely labor-intensive task. If you cannot afford the labor for annotation, many might think, "Can't we annotate with automatic tools?"
However, the process of language annotation is difficult to automate completely, particularly because of its precision and complexity. Language is rich in expressions that depend deeply on context, such as irony, metaphors, and idioms. To accurately understand and annotate these expressions requires a deep understanding of context, which cannot be fully reproduced by current automation technology.
Moreover, language is constantly evolving and changes significantly according to cultural backgrounds. As new words and expressions are born, annotation work needs to be continuously updated with human intervention. Furthermore, understanding expressions unique to specific cultural spheres or regions requires human knowledge familiar with that culture.
While automatic tools are useful as support for annotation work, they cannot completely replace these tasks at the current stage.
The reasons why annotation is difficult to automate are explained in detail in the following article.
"Why is annotation difficult to automate? When is manual annotation necessary?"
4. Precautions When Performing Language Annotation
When performing language annotation, please pay attention to the following points:
- Prepare as much data as possible
- Collect unbiased annotation data
- Assign annotators suited to the specialized field
Each of these is explained below.
- Prepare as much data as possible
To build high-precision NLP models, it is necessary to secure both the quantity and quality of learning data. Both are important elements, but since quality cannot be raised if the quantity is fundamentally low, try to secure plenty of data.
The richer the data, the better the model becomes at handling diverse topics and expressions. Therefore, a dataset should include as many samples as possible. If there is little natural language data, it will result in a model that can only handle a narrow range of topics.
It is desirable for the dataset to include different writing styles, genres, and topics. This allows the model to acquire the ability to handle various contexts and expressions. Aim for a highly versatile model by letting it learn as much data as possible.
- Collect unbiased annotation data
If there is bias in the annotation data, the model trained based on it may produce biased results. Therefore, it is important to equally include data with different contexts, backgrounds, and emotions.
Since models built through biased learning tend to have lower precision, it is crucial to collect data in a balanced manner.
That said, it is extremely difficult to gather vast amounts of data in a balanced way. If you lack expertise in data, it is a good idea to consult a specialist before performing annotation to avoid wasting subsequent work.
Precautions when requesting annotation data collection are summarized in the following article.
"Things to keep in mind when requesting annotation data collection"
- Assign annotators suited to the specialized field
When annotating sentences where difficult terminology is used, request annotation from personnel with knowledge in the specialized field. If personnel unfamiliar with the terms perform annotation, the risk of oversights increases, and time will be needed to acquire the knowledge.
If it is difficult to prepare many annotators matching the specialized field, prevent errors by creating manuals or establishing a double-check system.
5. Application Examples and Capabilities of Natural Language Processing
Utilizing NLP enables you to perform tasks such as the following:
- Natural Language Generation
- Chatbots
- Translation
Each of these is explained below.
- Natural Language Generation
By utilizing NLP, new natural language can be generated. ChatGPT is a generative AI developed utilizing NLP technology.
Utilizing natural language generation realizes labor savings and automation of tasks involving natural language. For example, the writing of articles and reports can be automated. Furthermore, models that have learned programming code are capable of programming.
- Chatbots
By utilizing NLP, it is possible to develop conversation services such as chatbots. There are already many homepages of governments and companies that have introduced chatbots.
Furthermore, by combining voice recognition technology, interaction through voice is also possible. Voicebots that can converse through voice are already in practical use at restaurants and hotels. In addition, products are being developed as supportive dialogue systems for care recipients.
- Translation
By utilizing NLP, translation into other languages is possible. Equipping chatbots or voicebots with translation functions allows them to handle tourists from abroad. Furthermore, besides just translating, it is possible to have them summarize the text.
Additionally, depending on the training data, besides translating natural language, it is possible to translate programming code into another language. In this way, NLP can be utilized in a wide range of industries depending on your ideas.
6. Summary
Training data is essential for AI, and its high precision leads to high precision in AI. To create high-quality training data, there are various elements such as high-tech annotation, extensive specialized knowledge, and a solid management structure.
Because annotation work, such as tagging vast amounts of data, appears to be a simple task at first glance, companies sometimes suppress labor costs by requesting services from individual crowdsourced workers or offshore companies. However, in such cases, even if work content and rules are determined, problems arise where precision becomes low because workers cannot understand or lack knowledge, and training data of the desired quality cannot be obtained. Not only high-tech workers but also the creation of an organization that can manage them and achieve objectives and deadlines is necessary.
Additionally, in creating training data, it is not easy to completely eliminate human error even if annotation technology is high and a quality management structure is in place. By responding accurately and quickly when errors occur, reliable data can be built up.
By clearing these elements, high-quality training data can be created, leading to high-precision AI.
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