Natural language processing is just a part of the broad world of Artificial intelligence. It is an integral part of AI that many businesses have harnessed and are continuing to. Let’s further look into it to know what it entails and the benefits your business can reap by deploying NLP technology.
What is natural language processing?
Natural language processing is a branch of AI, whose main concern is to come up with machines that can understand and respond to text and speech like humans do.
Together with statistical, machine learning and deep learning models, NLP combines computational linguistics-rule-based modeling of human language to enable computers to process human language in form of voice and data and understand its full meaning.
NPL is behind computer programs that translate text from one language to another, respond to commands that are spoken and summarize large volumes of text quickly in real time. Examples of NLP use include GPS systems, speech-to-text dictation software, digital assistants and customer service chatbots.
Did you know? By 2025 the Global Natural Language Processing market size is anticipated to reach $29.5 bn.
Natural language processing tasks
The human language is filled with complexities such as homonyms, homophones, idioms, metaphors, sarcasm and variations in sentence structure. These ambiguities take humans years to learn yet programmers have to teach natural language applications to recognize and understand these variations accurately from the start.
This has led to the development of several NLP tasks to break down voice data and human text in ways the computer can make sense of what it is ingesting. Some of these tasks include:
This task is also referred to as speech-to-text and entails converting voice data to text data. Any application that answers spoken questions or follows voice commands uses speech recognition. This task is quite challenging because of the different ways people talk. Some talk quickly, others slurring words, others with different accents with varying emphasis and intonation.
Part of speech tagging
This task is also known as grammatical tagging. It is the process of determining what part of speech a word is in a text based on its use and context.
Word sense ambiguation
This is the selection of the meaning of a word that has several meanings through a process known as semantic analysis, which determines the word which makes most sense in a given context.
Named entity recognition
It identifies words as self-entities. For example “India” is recognized as a country and the word “John” is recognized as somebody’s name.
This is the task of identifying when and if two words refer to the same entity. A good example is when a pronoun has been used in a text.
This task attempts to extract sentiments like emotions, suspicion, confusion or sarcasm in a text.
Natural language generation
This is the opposite of speech-to-text. It converts text form structured or unstructured data to human language. It can further be subdivided into extractive NLG and abstractive NLG.
- Extractive NLG
This approach takes text from a large body text and pulls out the sentences that represent the key points most and uses them to generate a summary of the larger text.
- Abstractive NLG
This approach creates text by first identifying key concepts and then generating new sentences that capture the key points of the text body.
Benefits of natural language processing
There are many benefits that NLP can bring to your business. Below are a few of them:
Better data analysis
It’s difficult for computers to process unstructured data such as documents, research results and emails. But with NLP technology large amounts of data can be analyzed and processed. Repetitive and tedious tasks can be completed accurately when NLP is deployed.
In staff recruitment, companies can also deploy NLP to screen applicants for specific desired characteristics. Moreover, this makes the process quicker as well as more effective.
Professional companies like accounting firms and law firms review large amounts of information. By deploying NLP technology in their operations they can reduce the time spent looking specific data. Chatbots can be trained to find specific information from multiple documents without necessary requiring human intervention. This frees the staff to concentrate on other issues ensuring efficiency in the organization.
Improved customer experience
To understand customer behavior and learn customers experience when they interact with your business, NLP technology can be deployed. NLP can be deployed to recognize customer sentiments in their messages using specific algorithms to understand the emotions in there text.
Staff can also find information quickly, because the technology processes big real-time data across multiple sources to produce more comprehensive data. This data can be used to answer customers satisfactorily and can also be used to effectively fulfill other duties they have been assigned to as employees.In the process there is employee satisfaction and engagement which is a plus for any business as employees are the first brand ambassadors of a company.
Improved efficiency and effectiveness always translates to one thing, reduced costs. The more effective the internal working systems of an organization are the less time the employees take to perform roles assigned to them. More is able to be achieved within a short time resulting to lower costs in attaining certain milestones. For example when NLP is applied in customer service support you will find that instead of needing six customer service representatives, two can perform their functions assisted by NLP technology. This translates to lower costs since even the number of employees reduces.
Natural language process cases
- Spam detection
- Machine translation
- Virtual agents and chatbots
- Social media sentiment analysis
- Text summarization.
To reap the maximum benefits of NLP technology, organizations must first evaluate their culture to ensure that their corporate culture is prepared for anxiety that comes with change. They must also ensure they comprehend elements of natural language like AI requirements, neural networks, semantic analysis, predictive analysis, machine learning algorithms, and summarization among others. They also need to consider the availability of resources to deliver the necessary data before starting the NLP project. This is due to the fact that it requires large volumes of data it can process. Once all the basic have been sorted now your business can reap and enjoy all the benefits that NLP technology has to offer.
Finally, connect with natural language processing company Aalpha to know more.
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