Natural Language Processing (NLP) uses a variety of techniques and methods to process and analyse human language. However, the whole process of building an NLP model can be classified as the culmination of three processes - computational linguistics, deep learning, and (lots of) statistics.

Computational linguistics refers to combining natural language and computational approaches to linguistic questions. Deep learning is a subfield of artificial intelligence that uses neural networks to learn from large amounts of data and make predictions based on these data. Statistics is also used in NLP to refine the training data, develop algorithms, and build models on top of these training data and algorithms.

Over recent years, the field of NLP has seen a wide range of applications. Some prominent applications of NLP used by modern enterprises are listed below:

• Chatbots and Virtual Assistants: NLP is being used to develop chatbots and virtual assistants like ChatGPT and Amazon’s Alexa respectively. The understanding of NLP allows these applications to interact with customers, providing them with information and assistance in a natural way that is understandable by humans.
• Speech Recognition: NLP often deals with the tasks of converting spoken words into text. This technology is used in applications such as text-to-speech, automated call centres, and even voice-enabled verification systems.
• Email Filtering: With the power of NLP, one can teach a system to filter and categorise emails based on their content. Moreover, by reading the content of emails, these models can label the messages as well. This helps in prioritising emails and reducing the time spent on email management.
• Language Translation: This was the main foundational reason for which NLP came into existence. NLP translation software can be used to translate text from one language to another. This technology is used in applications such as Google Translate and other language translation software.
• Proofreading: NLP leverages its language skills to enhance grammar-checking software and autocorrect functions. This technology is used in applications such as Grammarly and other grammar-checking software, and modern-day autocorrect apps.

On the technical front, there are three steps taken to train an NLP: data acquisition, data training, and data fine-tuning. The model takes a text input (raw data) and splits it into smaller units that the machine can understand. The model then extracts useful information from these units and applies machine learning algorithms to process it. The machine repeats this process with some adjustments to improve the output, and the machine learns progressively.

Techniques such as Tokenization, Stemming, Lemmatization and Sentiment Analysis are used to produce texts that are comprehensible to humans.

Here are some of the well-known NLP models and frameworks:

The good news is, there are many ways through which you can overcome these shortcomings.

First, you can improve the quality and diversity of your training data. This will help your model avoid hallucination and misinformation, as it will have more and varied data to learn from.

Moreover, identifying and reducing the inherent biases in the data also makes these models less susceptible towards generating erroneous responses.

Second, you can use better regularisation methods. Regularisation methods are methods that help prevent overfitting and other issues in the AI model that could lead to hallucinations.

Additionally, employing adversarial training and reinforcement learning also improves the robustness of NLP models and reduces the risk of hallucinations.

Third, you can apply more constraints on the model's output, such as limiting the length of responses or limiting the scope of facts. This will help your model generate more relevant and accurate responses.

As we can see, AI can offer many benefits for your organisation. However, the correct implementation of AI in your organisation is important.

It may seem easy to use AI-based solutions for various tasks, but the reality is that NLP apps, like any other deep learning applications, are not always reliable. The reason for this is that the more data and complexity you have, the more unpredictable the deep learning architecture becomes.

The Diversity in AI and NLP, and interest in this field over the decade, has been growing. Source: kbvresearch.com

Right now, NLP is one of the most important and influential fields in the age of AI. However, NLP also faces many limitations and challenges that need to be addressed.

To make the best use of NLP, one needs to understand how the NLP model works and design the optimal way to implement it in the organisation.

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