Use of Natural Language Processing in Business By Aditya Abeysinghe

By Aditya Abeysinghe

Today, AI (Artificial Intelligence) can be seen in every digital product/service. Using text and other types to process human language and then output a set of processed information has been common with wide use of automated AI services. This process, called NLP (Natural Language Processing), has improved user experience across many apps people use. Business use of NLP have been a new method of providing customers with the ability to search products with less effort and time.

What is Natural Language Processing?

Natural language processing is a type of AI used to understand the human language: text and spoken words and process them using AI techniques. Machine learning and other techniques are used to process input data and then provide an output. NLP is the method that is used by text-to-text or speech conversion systems, digital assistants that convert text from one form to another, etc. Some systems also generate new text based on processed data or recognize text from voice files or videos and then generate output.

What are the methods used?

Though computers deal with 1s and 0s, human language is composed of various language definitions. Mapping these two models either way is a difficult task as it requires conversions during input and output. Therefore, different methods are used to convert these models.

Speech or grammar tagging is the process of identifying grammar in text or voice. Nouns, verbs and other grammar in text or voice needs to be separated from other text and understood during NLP. NLP methods using machine learning can also classify words whether they are names, places, etc. Usually, a machine learning model trained using datasets can be used to classify text.

Detecting meanings of words based on the overall sentence is another method. The same word can have different meanings depending on the structure of the sentence. As an example, “Train the person” is an action, “When is the train to Madrid?” is a time. Therefore, before identifying the meaning of each word, meanings of words based on the structure of the sentence need to be categorized.

What are the approaches?

Many approaches are used in processing text or voice. One common approach is to use a deep learning method such as Convolution Neural Networks that can train a model with several iterations and improve the accuracy. These methods can be used for types such as structured data and unstructured data and methods such as clustering based on classes in data. Voice conversion methods often use trained models to map the voice with text. AI algorithms can be used for such methods.

Where is NLP used?

A common use case of NLP in business is in voice assistance tools. Many businesses use speech recognition to detect user voice and then output results as text or other types of data. These voice assistants are used in mobile apps, and they often display results of the place, quantity or images of different product categories sold for the user’s query. Some businesses also use voice assistance in applications to improve user interaction with techniques such as store-based and destination-based tours.

Another use of NLP is in translating of text from one language to another. Some translation apps also enable users to input voice or webpages and then translate it to another language. During this translation, the query is converted to text and then an AI model is used to map words based on the query, original language, and the language in which the output was required.

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