Natural Language Processing: Challenges and Applications

Have You Ever Wondered How Computer Programs Understand Human Language? 

Well, It is made possible because of Natural Language Processing (NLP). NLP is a branch of Artificial Intelligence (AI) that understands and derives meaning from human language in a smart and useful way. It assists developers to organize and structure data to execute tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

According to Statista, the worldwide revenue from the Natural Language Processing (NLP) market is forecast to increase rapidly in the next few years. It is predicted to be almost 14 times larger in 2025 than it was in 2017, increasing from around three billion U.S. dollars in 2017 to over 43 billion in 2025.

If you are interested in learning more about NLP, then you have come to the right place. In this blog, we will read about how NLP works, the challenges it faces, and its real-world applications.

Let’s begin!

What is Natural language processing?

As mentioned before, Natural Language Processing is a field of AI that studies the rules and structure of language by combining the power of linguistics and computer science. This creates intelligent systems which operate on machine learning and NLP algorithms and is capable of understanding, interpreting, and deriving meaning from human text and speech.

NLP tools use text vectorization to convert the human text into something that computer programs can understand. Then using machine learning algorithms and training data, expected outcomes are fed to the machines for making connections between a selective input and its corresponding output. This data is then saved in knowledge banks of machines for future use.

As per MARKETS AND MARKETS, the NLP market is segmented into the various verticals, particularly BFSI, IT, telecom, retail, eCommerce, healthcare, life sciences, transportation, logistics, government and public sector, energy, manufacturing, education, travel, hospitality, media, and entertainment. Among all, the healthcare and life sciences vertical is expected to grow at the highest CAGR during the forecast period of 2020-2026.

natural language processing

Challenges of Natural Language Processing

There are numerous challenges faced during the implementation of Natural Language Processing. Most of them are due to the fact that human language keeps on changing with time and is somewhat inconsistent and ambiguous. Even humans struggle to analyze and classify human language correctly. So how can we expect a machine to understand everything?

Let’s go through some examples of the challenges faced by NLP and their possible solutions to have a better understanding of this topic.

Text Related Challenges

As human language is an ever-evolving language, it is tricky for computer programs to understand its abstract use. Such as:

  • Sarcasm –
    Irony and sarcasm act as a challenge for machine learning models because sarcasm sentences generally use words and phrases that, strictly by definition, may be positive or negative, but connote the opposite. It becomes challenging for machines to understand the real meaning behind the words but it can be improved by training machines with certain cues that frequently accompany ironic or sarcastic phrases, like “yeah right”.
  • Synonyms –
    Synonyms are different words with similar meanings. Humans use different words of the same or slightly different meanings to express the same idea and this is where it becomes challenging for computer programs to understand them in the same context. To improve this, NLP systems can include all of a word’s possible meanings and all possible synonyms.Similar to synonyms and sarcasm, ambiguity, contextual words, phrases, and homonyms are other challenges of NLP.
  • Error in text and speech –
    Errors in texts and speeches are another challenge of NLP. Numerous times users make spelling mistakes and speak a word with the wrong pronunciation which makes it difficult for NLP to understand the user’s intention.This can be only solved with time as with the growth of language databases and smart assistants, it can be trained by their users, thus minimizing issues like this.
Data Related Challenges

Along with text related challenges, NLP faces various challenges due to data-related issues, such as:

  • Lack of research and development –
    Machine learning requires a lot of data and countless pieces of training data to perform. The more data NLP models are trained on, the smarter they become.Lack of data is the reason behind the text-related challenges of NLP which can only be solved when there is enough data to understand each and everything of a human language.Similarly, languages that are spoken by people with less access to technology often go neglected and underprocessed. Such as there are over 3,000 languages in Africa, alone and there simply isn’t very much data on many of these languages.

Applications of Natural Language Processing

NLP is around us more than we realize. Want to know more?

Let’s go through some of the top applications of NLP:

  • Search autocorrect and autocomplete –
    Have you ever wondered how the google search bar shows you suggestions or autocorrect the spelling when you search for something?natural language processing
  • Well, this is where NLP comes into the picture. With the data stored and language model, NLP helps us find accurate results much more efficiently. Not only Google but various other companies have also started using this feature on their websites, like Facebook and Quora.
  • Language translators –
    Companies like Google and Facebook use machine translation almost every single second. Google Translate goes through 100 billion words per day, while Facebook uses machine translation to translate text in posts and comments automatically, in order to break language barriers and allow people around the world to communicate with each other. These smooth translations are possible because of NLP.NLP has improved the machine translation systems. Earlier, machine translation systems were dictionary-based and rule-based systems and had very limited success. However, due to progression in the field of neural networks, availability of humongous data, and powerful machines, machine translation has become moderately accurate in translating text from one language to another.
  • Customer assistance –
    NLP has a great role to play in enhancing the customer support system. With the help of NLP, chatbots can solve most of the customer queries of their own, leaving a very little amount of work for employees to do. This way employees can focus their time and energy on far more important tasks, thereby increasing work efficiency.
  • Survey analysis –
    Performing regular surveys is the best way to evaluate a company’s success. However, it is very difficult to analyze the large amount of data generated during these surveys. This is where companies use NLP to analyze the surveys and generate insights from them, like identifying the opinions of users about an event from the feedback and examining product reviews to understand the pros and cons.

Wrapping up

Natural Language Processing plays an essential part in technology and the way humans interact with it. Though it has its limitations, it still offers huge and wide-ranging advantages to any business. With new techniques and technology cropping up every day, many of these barriers will be broken through in the coming years.

If you are looking for Natural Language Processing services providers, Jellyfish Technologies is the right choice for you.

Jellyfish Technologies is one of the leading NLP service providers. We offer standard solutions for processing and organizing large data using advanced algorithms. Our dedicated development team has strong experience in designing, managing, and offering outstanding NLP services.

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