Role of Machine Learning in Natural Language Processing
Natural language processing refers to the technical branch of computing, or artificial intelligence services, that is concerned with giving computer systems the ability to understand digital text and spoken feedback, in the same way that humans can.
Natural language processing combines computational linguistics, rule-based modeling of human languages, statistical modeling, machine-based learning, and deep learning benchmarks. Together, these advanced technologies allow computer systems to process human languages through voice or text data. The desired outcome or purpose is to 'understand' the full meaning of the respondent's message, along with the speaker's or writer's goal and belief.
Natural language processing operates within computer programs to translate digital text from one language to another, respond appropriately and sensibly to spoken commands, and summarize large volumes of information. All within the limits of real-time processing.
Role of Machine Learning in Natural Language Processing Applications
As with the natural language processing task, Applications of machine learning and deep learning algorithms have played a huge role in almost all natural language processing applications. In recent times there has been renewed research interest in these fields due to the ease with which machine learning and deep learning algorithms can be implemented, and this is especially true for deep learning techniques.
1. Sentiment Analysis
Sentiment Analysis strives to analyze users' opinions or feelings about a certain product. Sentiment analysis has become a very important part of customer relationship management. Even a single negative opinion can be disastrous for the product. Recent times have seen increased use of deep learning techniques for sentiment analysis.
An interesting fact to note here is that new deep learning techniques have been developed especially for sentiment analysis, which is the level of research that is being done for sentiment analysis using deep learning.
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2. Chatbot Systems
Chatbot systems are conversational agents or dialogue systems that attempt to engage the user in a conversation. This conversation can be through voice or text. Personal assistants like Amazon Alexa and Google Assistant have popularized chatbot systems and have also shown the level of ease through which user interaction can take place.
As easy as it may seem, developing a true AI chatbot system that can replace a human agent is an extremely difficult task. Which requires natural language understanding and also natural language generation.
Recent frameworks like Google's DialogFlow, IBM's Watson AI, and Amazon's Alexa AI provide an easy way to build a chatbot system. And all of these frameworks employ complex and proprietary deep-learning architectures.
3. Question Response Systems
As the name suggests, a question-and-answer system is a system that tries to answer questions from users. In recent times, the fine line between a dialog system and a Q&A system becomes blurred and most of the time, a chatbot system performs the Q&A task and the opposite is true as well. Therefore, research papers that undertake to develop a chatbot system will, in all probability, also develop a question-and-answer system within it.
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4. Information Retrieval Systems
Information retrieval is another important application of natural language processing that attempts to retrieve relevant information. Information retrieval systems act as the backbone for systems like chatbot systems and question-answering systems.
The most basic way to retrieve any data is to use the frequency method where the frequency of the keywords determines whether or not a particular piece of data is retrieved. But, smart systems process the required query as well as the big data present to retrieve only the relevant information. This process is carried out using deep learning techniques.
5. Machine Translation
A machine translation system strives to translate a text from one language to another with little or no human intervention. Apps like Google Translate are one of the best examples of the machine translation system.
Having a translation system that translates word for word is not enough as sentence construction can vary from language to language. For example, English follows the Subject-Verb-Object format while Hindi follows the Subject-Object-Verb format for sentence construction. Other than this, there are many different rules that need to be followed. All these things make the task of machine translation difficult.
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Advantages of Natural Language Processing and Machine Learning
Like other technical forms of artificial intelligence, natural language processing and machine learning present advantages and challenges.
The advantages of NLP include the following:
Once successfully implemented, the use of natural language processing/machine learning systems becomes less costly over time and more efficient than employing skilled/manual labor.
Natural language processing helps companies offer more immediate customer service with better response times. Regardless of the time of day, both customers and potential leads will receive direct answers to their queries.
Pre-trained machine learning systems are widely available for expert developers to optimize different natural language processing techniques, making them easy to implement.
While advances in natural language processing are certainly promising, there are specific challenges that need to be considered.
Conclusion
Natural language processing is the practice of teaching machines to understand and interpret conversational input from humans. Machine Learning-based NLP can be used to establish communication channels between humans and machines. Although it is continually evolving, NLP has already proven itself useful in multiple fields. Different types of natural language processing implementations can help businesses and individuals save time, improve efficiency, and increase customer satisfaction.
Combined with machine learning algorithms, NLP creates systems that learn to perform tasks on their own and improve through experience. NLP-based tools can help you rank social media posts by sentiment or extract named entities from business emails, among many other things.