5 Important Role of Machine Learning in NLP (Natural Language Processing)

Introduction

Natural language processing (NLP) is a necessity for both our personal and professional lives as a part of our daily existence. But, have you ever thought about how you interact with your phone to carry out tasks? Or, how voice assistance gives exact results. This is common these days, due to voice assistants like Siri, Alexa, Google Assistant, and others. There is a crucial role of machine learning in NLP.

Machine learning (ML) and NLP operate by speech-activated programs to respond to our voice commands to call people. Moreover, you can add events to your calendars and answer inquiries. Hence, NLP changes the way we work, live, and play besides making our lives easier. Let’s explore more about the important role of machine learning in NLP, but prior we discuss what is the difference between these two.

Definition of Machine Learning and Natural Language Processing

Natural language processing, machine learning, and artificial intelligence all have various definitions. However, many of us use them interchangeably. AI, a catchall phrase for machines that can simulate human intelligence, includes both NLP and machine learning.

Artificial Intelligence

It is a branch of computer science that enables computers to tackle problems to ease human life. AI has a wide range of applications in modern society. Furthermore, NLP and ML are both a part of AI.

Natural Language Processing

NLP is a type of AI that enables machines to read and understand human language in addition to reading it. It can help computers to carry out tasks like automatic speech recognition, sentiment analysis, and text summarization. Moreover, computers use NLP to understand spoken or written material.

Machine learning

It is an application of AI that gives systems the capacity to learn from experience and advance without explicit programming. Hence, ML helps address AI difficulties and enhance NLP by automating procedures and providing precise responses.

Role of Machine Learning in NLP 

Machine learning and deep learning algorithms play a critical role In practically all of the applications of natural language processing. ML and deep learning algorithms are so simple to implement. Moreover, there has recently been a resurgence in academic interest in these areas, particularly in deep learning methods.

Let's take a closer look at some applications of NLP where deep learning techniques have been really helpful.

1. Chatbots Systems

Chatbots are conversational agents or dialog systems that try to engage the user in conversation. Thus, you can have this chat via voice or text. For instance Personal assistants like Amazon's Alexa and Google Assistant. They also show how easy user participation can be, and are helping chatbot systems gain popularity.

Even though it seems easy, building a real chatbot system that can replace a human agent is a difficult task. Both natural language generation and natural language understanding are vital in this. This is one of the most useful combos of NLP machine learning.

2. Sentiment Analysis

You can investigate a product's user attitudes or views using sentiment analysis. Moreover, sentiment analysis now has a lot of weight in customer relationship management. Even one negative review can have devastating effects on the product. Also, there is a rise in the use of deep learning techniques in sentiment analysis in recent years. It is noteworthy to observe the level of research on sentiment analysis using ML. The research requires an extensive role of Machine Learning in NLP.

3. Question Answering Systems

A question-answering system is, as its name implies, a system that aims to respond to users' queries. Also, the distinction between a dialog system and a question-answering system has become increasingly hazy. Nowadays, chatbot systems often handle the question-answering function, and vice versa. 

Therefore, it stands to reason that research projects promising to create a chatbot system will also create a system for answering questions. NLP and machine learning help in the processing of questions, information retrieval, and answers. These are the three key parts of a question-answering system. Hence, these three elements use machine learning and deep learning techniques. 

4. Machine Translation

With minimal to no human intervention, an automatic translation system seeks to translate text from one language to another. Systems like Google Translate are among the best illustrations of machine translation. A translation system that translates words for words is inadequate since sentence structure may vary from language to language. This role of machine learning in NLP is more popular than others.

5. Systems for Information Retrieval

Another significant use of natural language processing that seeks to extract pertinent information is information retrieval. The skeleton of systems like chatbots and question-answering systems is an information retrieval system. The basic approach to information retrieval is the frequency method. Here, the frequency of keywords decides whether or not a specific piece of data recovers. Smart systems analyse both the current vast amount of data and the necessary query to get the pertinent data. Deep learning methods are very useful in this procedure.

Key Takeaways on The Role of Machine Learning in NLP
 

• Artificial intelligence (AI) in the form of Natural Language Processing (NLP) enables robots to understand and read human language. Thus, NLP enables computers to learn written or oral language.
• Both benefits and drawbacks are there with the usage of natural language processing. Thus, when you use NLP in businesses, you gain benefits like low prices and fewer client wait times. Hence, it results in customer satisfaction. However, training takes time, and ML is never completely dependable.
• NLP is a field that is constantly expanding with applications like translation and speech recognition. Moreover, it helps in sentiment analysis, question-and-answer systems, chatbots, market intelligence, automatic text classification, and automatic grammar checking. 

Conclusion

Teaching machines to read and interpret human conversational inputs is Natural Language Processing. Humans and machines can communicate with one another using NLP and machine learning. NLP continues to show its value in numerous sectors despite its ongoing evolution. Thus, the role of machine learning in NLP can help people and companies save time, work more effectively, and satisfy customers.

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