Do you know what Natural Language Processing (NLP) is? It is the technology that enables AI to analyze human language, understand conversations, and generate text, and it is being utilized in various business scenarios.
In this article, we will comprehensively explain the overview, mechanisms, capabilities, and application examples of NLP. If you are considering implementing AI in your company, please read through to the end.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a technology that enables AI to understand, analyze, and effectively utilize human language. By treating the words we use daily (natural language) as data, it makes these things possible.
A major characteristic of NLP is its ability to understand not only the structure of words and grammar but also context and nuances. This allows NLP to be used in various scenarios, such as question-answering in search engines, multilingual translation in translation apps, and conversations with chatbots.
In this way, NLP can be considered a crucial technology for enabling smooth communication between AI and humans.
Types of Natural Language Processing (NLP) and Their Capabilities
Natural Language Processing (NLP) can be broadly divided into two categories: “Natural Language Understanding (NLU)” and “Natural Language Generation (NLG).” Let’s take a closer look at the differences between them.
Natural Language Understanding (NLU)
Natural Language Understanding (NLU) is the technology that allows AI to comprehend human language. Specifically, it analyzes the words, grammar, and context contained within sentences and conversations to interpret the underlying intent and meaning.
Through the application of NLU, AI can consider the ambiguity and polysemy of language and perform accurate interpretations. In other words, NLU plays the role of the “receiver” of language, handling tasks like text classification, sentiment analysis, and intent recognition.
Natural Language Generation (NLG)
Natural Language Generation (NLG) is the technology that enables AI to produce natural-sounding sentences and conversations, much like a human would. It is used in various scenarios, such as the automatic creation of news articles or chatbots that generate appropriate responses to users.
By using NLG, AI can construct grammatically correct and coherent natural sentences based on input data and information. Thus, NLG serves as the foundation for AI to act as a “sender” of language.
Application Examples of Natural Language Processing (NLP)
Recently, Natural Language Processing (NLP) has been utilized in a wide range of business scenarios. This chapter introduces five representative use cases of NLP.
Conversational AI
One of the most representative use cases of Natural Language Processing (NLP) is conversational AI. By applying NLP to AI chatbots or virtual assistants, they can respond to user questions and requests in natural language.
Specifically, the system works by using Natural Language Understanding (NLU) to analyze the intent of the question and Natural Language Generation (NLG) to generate an accurate response. NLP is used in a wide range of situations, from handling customer support inquiries to processing voice commands through smart speakers.
Machine Translation
Machine translation, which performs translation between different languages, is also a significant application field of Natural Language Processing (NLP). For example, Google Translate, a representative translation tool, uses NLP to analyze text and provide translation results that consider grammar and context.
Especially since the introduction of models based on the previously mentioned Transformer, it has become possible to perform natural and highly accurate translations. In this way, NLP forms the foundation supporting global communication across language barriers.
Text Mining
Text mining, which extracts valuable information from large volumes of document data, is widely used in various business and research fields. For example, it can analyze posts on social media to grasp consumer sentiments and trends, or extract essential information from legal documents or academic papers. This enables efficient acquisition of insights to support decision-making.
Related article: What is Text Mining? A Simple Explanation of the Mechanism and Typical Analysis Methods!
Text Summarization and Classification
Text summarization and classification, such as summarizing news articles or long texts, and categorizing emails or reviews, are applications of Natural Language Processing (NLP) that aid in information organization. Specifically, AI extracts the important parts of a text to create a concise summary, or assigns appropriate tags based on the content. In our age of information overload, NLP is a vital technology supporting efficient information gathering.
Text Generation
Natural Language Processing (NLP) enables the efficient generation of high-quality text. Examples include generating captions based on images or creating creative texts like novels and poems. Furthermore, advanced models like the GPT series can produce natural-sounding text that seems human-written, leading to applications in entertainment and marketing.
The Mechanism of Natural Language Processing (NLP)
So far, we have introduced the overview and application examples of Natural Language Processing (NLP), but how does NLP actually work? In this chapter, we explain the mechanism of NLP.
Machine-Readable Dictionary
The foundation of Natural Language Processing (NLP) is the machine-readable dictionary, a database of word meanings and relationships. Machine-readable dictionaries store definitions, parts of speech, synonyms, antonyms, and more, serving as a guide for AI to understand language. By utilizing this, AI can interpret words based on their basic meanings and context.
Corpus
A corpus is a large dataset of actual language data. Examples of corpora include newspaper articles, blogs, and social media posts. AI learns how words and phrases are used by analyzing corpora, making corpora a critical element that significantly impacts the performance of Natural Language Processing (NLP).
Morphological Analysis
Morphological analysis is the process of breaking down sentences into their smallest meaningful units, such as words or phrases, and identifying the part of speech and role of each. For example, the sentence “The cat walks” would be broken down into “The (article),” “cat (noun),” and “walks (verb).” By performing morphological analysis, Natural Language Processing (NLP) clarifies the basic structure of a sentence, thereby improving the accuracy of subsequent processing.
Parsing (Syntactic Analysis)
Parsing, or syntactic analysis, is a technique for analyzing the relationships between words in a sentence to clarify its grammatical structure. For example, in the sentence “The cat chases the mouse,” “cat” is identified as the subject, “chases” as the predicate, and “mouse” as the object. Performing syntactic analysis allows AI to understand the overall meaning of a sentence more precisely.
Context Analysis
Context analysis is a technique for analyzing not just words and phrases, but also their meaning within the overall sentence or conversation. For example, if the word “bank” appears in a sentence, the context determines whether it refers to a financial institution or the side of a river. By considering context to interpret meaning, AI can achieve more natural conversations and text generation.
Representative Models Used in Natural Language Processing (NLP)
Even when we simply say “Natural Language Processing (NLP),” the models used are diverse. In this chapter, we pick and introduce five representative models used in NLP.
word2vec
word2vec is a technique for representing words as vectors (sets of numerical values), allowing the numerical capture of word meanings, similarities, and relationships. For example, it enables calculations like “king – man + woman = queen.” A characteristic of word2vec is its lightweight and fast nature, leading to its use in a wide range of applications, including text classification, sentiment analysis, and search engine optimization.
doc2vec
doc2vec is an extension of word2vec, with the major characteristic being its ability to vectorize entire sentences or documents, not just words. By using doc2vec, it becomes possible to quantify the features of a text, measure similarity between documents, and perform document classification. For instance, it is used for categorizing news articles or reviews, serving purposes that require understanding the overall meaning of a text.
RNN
RNN (Recurrent Neural Network) is a type of neural network (AI technology designed by mimicking the neural circuits of the human brain) that processes input data while considering its sequential nature. It maintains past data in an internal state and reflects it in subsequent processing, thereby learning the flow of context. For example, RNNs are a valid option for analyzing short sentences or performing simple time-series prediction.
LSTM
LSTM (Long Short-Term Memory) is a type of RNN specifically designed to capture long-range dependencies in time-series data or extended contexts. While the vanishing gradient problem is a major challenge for standard RNNs, using LSTM enables Natural Language Processing that considers long-term dependencies. It is suitable for tasks where understanding the meaning of long texts or temporal flow is crucial, such as chatbots and machine translation.
Note: The vanishing gradient problem refers to the issue where, during the learning process of a neural network, the gradients (differential values) used to update weights by backpropagating errors become extremely small, causing learning to stall in deeper layers.
Transformer
The Transformer is a model that has become the mainstream in current Natural Language Processing (NLP). It efficiently understands context by utilizing a Self-Attention mechanism. The Self-Attention mechanism allows all words in an input sentence to evaluate the relationships and importance among each other.
Because Transformers achieve high accuracy in tasks like translation, summarization, and text generation, they serve as the foundation for advanced models such as GPT and BERT. Unlike traditional RNNs and LSTMs, Transformers allow for parallel processing and offer high computational efficiency, demonstrating overwhelming performance in tasks involving large-scale data.
Points to Note When Using Natural Language Processing (NLP)
While Natural Language Processing (NLP) is a very convenient technology, there are several points to keep in mind when actually using it. This chapter introduces three precautions when utilizing NLP.
Beware of Data Bias
Typical Natural Language Processing (NLP) models learn based on their training data. Therefore, if the data used contains biases, there is a risk that the model will inherit those prejudices or make incorrect judgments.
A concrete example could be a case where the model reflects biases related to specific genders or regions. To prevent this issue, it is crucial to select diverse data and regularly evaluate and correct the model.
Understand the Model’s Scope of Application
While Natural Language Processing (NLP) is an excellent technology, it may not produce accurate results for tasks that fall outside its scope of application. For instance, general-purpose models might not fully comprehend legal documents or medical data rich in specialized terminology.
Therefore, when utilizing NLP, it is important to check the model’s application scope beforehand. If you need to perform tasks beyond its scope, creating a custom model suited to that specific content can maximize the effectiveness of AI implementation.
Consider Privacy and Security
Since Natural Language Processing (NLP) may process data containing personal information, it is essential to consider privacy and security. For example, when chatbots or voice assistants handle personal data, there are risks of inappropriate use or information leaks. To maintain user trust, it is important to thoroughly implement measures such as data anonymization, encryption, and clearly defining the purpose of data usage.
Conclusion
In this article, we explained the overview, mechanisms, capabilities, and application examples of Natural Language Processing (NLP).
By utilizing NLP, companies can leverage it in various business scenarios, such as conversational AI and text mining. Re-read this article to solidify your understanding of the mechanisms and representative use cases of NLP.
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