What is Natural Language Processing (NLP)?

Lacan; “human resides in language” said because without this “transmissive” element that distinguishes us from other living beings, we could neither build a civilization nor imagine the future.

From the moment we are born, we start processing language and need to practice for years to make it correct.

In the later stages of our lives, we might think how difficult it is to learn a new language. It seems that artificial intelligence has somehow managed to do this. 🤔

If we ask ChatGPT to write a poem in the style of Edgar Allan Poe, it can generate a poem within seconds. Similarly, if we request it to prepare an informative LinkedIn post about software and programming, it can do so in seconds.

It sounds quite interesting. So how exactly does artificial intelligence understand language and respond?

Developments in information technology have encouraged scientists to work on languages. Initially, scientists aimed to communicate with computers, but over time, they tried to understand and evaluate spoken or written language.

This field, known as Natural Language Processing, generally works on processing languages with the help of computers.

In this article, we will examine what natural language processing is and the working principle behind it. Additionally, if you are curious about the recent history of natural language processing, we recommend checking out our other article.

 

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a branch of artificial intelligence and computer science focused on understanding and deriving meaning from human language. The goal is to program computers to process and analyze large amounts of natural language data. It would not be wrong to say that natural language processing is truly at the interface between computer science and linguistics.

Natural Language Processing can generally be divided into two main categories:

• Natural Language Understanding (NLU)

Natural Language Understanding is how artificial intelligence interprets text or speech. In fact, the word "understand" is a bit of a misnomer because computers do not inherently understand anything. Instead, they can process inputs in a way that leads to meaningful outputs for humans.

• Natural Language Generation (NLG)

Recently, the ability of computers to create language has garnered significant interest. In fact, the text component of generative artificial intelligence is part of natural language generation.

We can compare Natural Language Generation (NLG) to a complex guessing game. Instead of naturally understanding grammar rules, generative AI models create text through probabilistic models that consider the context of their responses.

Today's large language models (LLMs) are trained on a vast amount of text, so even if the content is sometimes incorrect, their outputs are generally good.

 

 

Let's get to the topic of natural language processing. ◀️ Natural Language Processing enables machines to perform a wide range of tasks with both spoken and written human language. For example, it helps ChatGPT catch and respond to syntax errors that you might not even notice.

Through Natural Language Processing, artificial intelligence applications perform the following tasks: 

• Language Generation: Artificial intelligence applications generate new text based on given commands or contexts, such as chatbots, virtual assistants, or even creative writing.
• Answering Questions: Artificial intelligence applications respond to users who ask questions in natural language about a specific topic.
• Sentiment Analysis: Artificial intelligence applications analyze text to determine whether it expresses a positive, negative, or neutral sentiment.
• Text Classification: Artificial intelligence classifies texts into different categories or topics. For example, it can categorize news articles into politics, entertainment news into entertainment, etc.
• Machine Translation: Artificial intelligence translates text from one language to another, for example, from English to Spanish.

These are just a few of the basic tasks that artificial intelligence can perform thanks to natural language processing.

So how does artificial intelligence reach the stage where it can perform these tasks? How does the underlying natural language processing process work? Let's take a look at this topic in the section below. 👀

 

How Does Natural Language Processing (NLP) Work?

Before a computer or artificial intelligence application can perform any of the tasks we listed above, it needs to understand how language works. This is done through a process called machine learning. In this process, large (we can say enormous) amounts of training data or language examples used in different contexts are transferred to the machine and taught.

To better understand what we mean by enormous, let's also provide this information; OpenAI's chatbot ChatGPT is trained on over half a trillion words from open websites, texts, articles, and data. 😧

Of course, it's not enough just to fill the machine with data. While words and sentences make sense to humans, they are just sequences of text for computers.

For the computer to understand them, human trainers label the data and teach the computer how to understand the language, learn the rules and structures, and how to analyze them. This process is carried out using natural language processing techniques.

Do you remember when we did sentence analysis in Turkish class? ✍️ That was syntactic parsing. We were breaking down the sentence into parts to identify elements like the subject, object, and verb. Artificial intelligence works in a similar way.

The techniques used in this process include:

• Tokenization: Breaking the text into smaller semantic units.
• Part-of-Speech Tagging: Categorizing words into types such as nouns, verbs, adjectives, etc.
• Lemmatization: Reducing words to their roots or base forms.
• Dialogue Management: Analyzing the style patterns in conversations.

These methods allow artificial intelligence to better understand language. When artificial intelligence masters these techniques, it can transform its linguistic knowledge into deep learning algorithms. Afterwards, it can not only read and understand text but even generate its own texts.

In short, this is what allows ChatGPT to generate text in response to requests like “write me a 100-word poem”.

To summarize how Natural Language Processing (NLP) works:

• A large amount of training data is provided to the computer.
• Humans label this data with language rules and teach natural language processing techniques such as tokenization.
• Then, using these techniques, they develop deep learning algorithms that form the foundation of the language model.
• Human trainers retrain the model using feedback methods.
• These algorithms enable tasks such as answering questions or generating new text.

 

Natural Language Processing Applications

Some of the main applications of NLP are:

 

Sentiment Analysis

Sentiment analysis is the process of classifying the emotional intent of a text. In a sentiment classification model, the input is a piece of text. The output is the probability that the expressed emotion is positive, negative, or neutral. Deep learning models are used to capture these probabilities. Sentiment analysis can be used to classify customer reviews on various online platforms.

 

Autocomplete

Autocomplete predicts what the next word will be. At this point, natural language processing compares what you have written before with a large database of what others have written in the past and can offer one or several predictions about what the next sentence should be. We can see this application in chat applications like WhatsApp and in predicting search queries like Google.

 

Classification

Another common use of natural language processing is categorizing different inputs. For example, natural language processing can determine which aspects of a company's products and services stand out.

 

Text Generation 

Natural Language Generation (NLG) produces text similar to that written by humans. These types of models can be used to generate text in various types and formats, including tweets, blogs, and even code.

 

Summarization

Summarization is the process of shortening text to highlight the most important information. Summarization is divided into two classes of methods: Extractive and Abstractive.

• Extractive Summarization: This method aims to create a summary by selecting the most important sentences from a long text and combining them.
• Abstractive Summarization: This method rephrases the text while creating a summary. It is similar to writing a summary that includes words and sentences not found in the original text.

 

Question Answering

Question Answering takes on the task of answering questions posed by humans in natural language. 

One of the most notable examples in this field is Watson, an IBM computer that outperformed its competitors in the 2011 Jeopardy! quiz show.

Question answering tasks are generally divided into two groups:

• Multiple Choice: In this type, a question and several multiple-choice answers are presented. The model's task is to select the correct answer.
• Open Domain: In open domain question answering, the model responds in natural language to a question asked without presenting options. Typically, these answers are obtained by querying a large amount of text.

 

Speech Processing

Converting spoken language to text presents challenges such as accents, background noise, and phonetic variations. Natural Language Processing significantly improves this process by using contextual and semantic information.

On platforms like Zoom or Google Meet, natural language processing allows for real-time transcripts based on new contexts from ongoing conversations.

Another example is voice responses in customer service. Natural language processing is used here to understand the topic for which help is requested.

 

Language Translation

Machine translation automatically translates between different languages. In such a model, a text in a specific source language is given as input, and the model outputs a text in the specified target language. Google Translate is perhaps the most well-known application. 

Effective approaches used for machine translation can accurately distinguish words with similar meanings. Some systems also perform language detection, meaning they can determine which language the text is in.
 

Examples of Natural Language Processing

Natural Language Processing enhances efficiency, accuracy, and user experience in healthcare, legal services, retail, insurance, and customer service. 

 

Healthcare

NLP assists in transcribing and organizing clinical notes. This ensures that patient information is documented accurately and effectively. 

Advanced NLP models can categorize patient information, identify symptoms, diagnoses, and prescribed treatments. This can simplify the documentation process, minimize manual data entry, and improve the accuracy of electronic health records.

 

Finance

Financial institutions leverage natural language processing to perform sentiment analysis on various text data such as news articles, financial reports, and social media posts to gauge the market sentiment about specific stocks or the market in general. 

Algorithms analyze the frequency of positive or negative words. Subsequently, machine learning models can predict the potential impact on stock prices or market movements.

 

Customer Service

There is a fact that chatbots powered by natural language processing revolutionize customer support by providing responses to customer inquiries 24/7. These chatbots understand customer queries through text or voice, interpret the underlying intent, and provide accurate responses. 

For example, a customer might want to inquire about the status of their order. In this case, the chatbot can integrate with the order management system to fetch the real-time status and relay it to the customer.

 

E-Commerce

Natural language processing significantly enhances the on-site search functionality of e-commerce platforms by understanding and interpreting user queries, even if they are expressed in conversational language or contain typos. 

For example, if a user searches for "syah elbse", NLP algorithms correct the typo and understand the intent to provide relevant results for "black dress." This allows users to find what they are looking for even with vague queries.

 

What is the Difference Between Natural Language Processing (NLP) and Large Language Models (LLM)?

Large Language Model (LLM) is a type of machine learning model that can understand human-generated text and produce natural-sounding outputs. Commonly used LLMs like ChatGPT are trained on very large text datasets.

Although the terms NLP and LLM are different concepts, they naturally have similarities. Both use machine learning and large datasets to interpret human language. In fact, some sources define LLM as a type of NLP.

However, LLMs differ from NLP models in several significant ways 🤚:

• While NLP is usually trained for a specific task, LLMs have a wide range of uses.
• While NLP provides insights and interpretations, LLMs generate statistically relevant text but may not ensure understanding of the underlying meaning.
• Because they have a very broad range of uses, training LLMs requires much more data and training than NLP models.

For example, an NLP model would be more useful for sentiment analysis, whereas an LLM would work better for a chatbot that interacts with customers. 

Let's give another example. While an NLP model can help a search engine interpret a user's query and generate relevant search results, an LLM can write its own response to a query based on a statistical analysis of existing relevant content.

 

NLP vs. LLM vs. Generative AI

NLP is different from generative artificial intelligence but is related to it. Generative artificial intelligence is a deep learning model that can generate text, audio, video, images, or code. In contrast, NLP models are generally not designed to generate text. We can summarize the difference in this way. By the way, since LLMs can also generate text in response to queries, they are a type of generative artificial intelligence. 

 

Discussions Related to Natural Language Processing

Although Natural Language Processing is a useful tool, it is not without its flaws. Natural Language Processing faces various disadvantages due to the complexity of human language.

• Ambiguity. Human language is often ambiguous, and words can have multiple meanings. This makes it difficult for NLP models to interpret the correct meaning in different contexts. To address this issue, NLP uses various methods such as context evaluation. Although methods are used to solve the problem, it remains an ongoing issue. 
• Context. Understanding the context in which words are used is crucial for accurate interpretation. Understanding the context of words is a major challenge for NLP.
• Ironicity. Detecting ironic and sarcastic texts is quite difficult.

Natural Language Processing has been at the center of many debates. While some focus directly on the models and their outputs, others focus on secondary concerns such as who has access to these systems and how their training affects the natural world. 

Researchers and developers are continuously working to overcome these challenges by using advanced machine learning and deep learning techniques to enhance the capabilities of natural language processing models and make them more proficient in understanding human language. 🏋️

 

Programming Languages, Libraries, and Frameworks for Natural Language Processing (NLP)

Many programming languages and libraries support natural language processing. We have listed the most preferred ones below 👇:

1. Python is the most widely used programming language for addressing natural language processing tasks. Most libraries and frameworks for deep learning are written for Python.
2. Natural Language Toolkit, abbreviated as NLTK, is a useful tool for Python that includes many libraries for natural language processing. It provides a range of text processing libraries for classification, tagging, lemmatization, parsing, and semantic reasoning.
3. spaCy is one of the versatile and open-source natural language processing libraries. It supports over 66 languages and provides pre-trained word vectors. spaCy can be used to build production-ready systems for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and more.
4. Popular deep learning libraries include TensorFlow and PyTorch are among them. These libraries are some of the most common tools for developing natural language processing models.
5. R Programming Language is widely used by data scientists and statisticians. NLP libraries in R include TidyText, Weka, Word2Vec, SpaCyR, TensorFlow, and PyTorch.
6. JavaScript, along with Java and Julia, there are many other languages that have libraries implementing NLP methods.

 

Example of Natural Language Processing (NLP) on ChatGPT

Many artificial intelligence applications that generate text do so by predicting patterns in human requests. Then, they respond with text that best matches the request. This is a natural language processing technique that allows predicting the probability of a word sequence in a sentence. This technique is often successful. 

Additionally, artificial intelligence chatbots analyze user intent based on dialogue management (another natural language processing technique). This allows them to simulate a conversation by looking at other dialogue examples in training data and mimic the same style. 

(Thanks, ChatGPT. :D 🫶)

In other words, natural language processing is what allows chatbots to have consistent conversations and understand user intent. 

However, while chatbots can identify nuances in language (such as sarcasm or slang), most of them need to be prompted to mimic these aspects.

(Aaa 🥲!)

As shown in this example, ChatGPT initially gave me a response that matched my first sentence and offered suggestions. To receive an unexpected, sarcastic answer, I had to input a prompt that instructed it to do so. 

 

Conclusion

Natural Language Processing is changing the way we communicate with technology, alongside real-world applications like chatbots, cybersecurity, search engines, and big data analysis. 

It is clear that natural language processing will be a part of our lives for a long time and will make our lives easier in many ways. 

Even if we don't individually use tools like ChatGPT, natural language processing appears in many areas of our lives, as mentioned in the examples above.