Implementing Automatic Natural Language Processing in Your Small Business Organization: Now or Never!

If you’ve adopted AI in your small or medium business before, congratulations. Otherwise, there is no better time than now. The urgency of immediately adopting and implementing some form of AI should be a top priority. Otherwise, there are inherent risks. First, you will become lagging behind and most likely obsolete, given the super cycle of innovation we are experiencing today – the costs associated with “not” implementing AI, including going out of business.

Implementing AI is quite different from other organization-wide strategies, as it involves very specific characteristics and pools of expert resources that SMEs might not have access to.

As one of the five current subcategories of AI, natural language processing or NLP has been around for some time. In fact, AI has been around since the 1940s. It tried to gain traction (starting and restarting for decades) and finally broke through in 2006 after a cold winter in the mid-1980s. With the explosion of data at Over the past two decades and the computing power of hardware, companies have finally figured out how to leverage AI. Companies that don’t start implementing AI, in its simplest form, are really getting stale, and for stakeholders, it’s non-negotiable. The challenge is to have a vision of what the AI ​​will accomplish, the overall cost of implementation, the associated risks, which include horrific user experiences, and get 100% buy-in across the organization. business. I

In this article, I will attempt to discuss implementation techniques in the purest form of AI, a subdomain called Natural Language Processing or (NLP), which uses computers to process human language. It’s ubiquitous now, but the implementation comes with some risks. This process enables computer systems to understand, interpret, translate and generate human-like language in both oral and written form. Intuitive understanding of human language on a general level is still beyond the capabilities of computers, but NLP is advancing rapidly as it learns from the explosion of data clusters that identify patterns used by humans in language.

Since we all have a creative side, check out these two sites that use NLP in an advanced form: authrors.ai and primer.ai.

NLP Techniques include, but not limited to, counting words quickly, counting word occurrences, for example in the narrative report of quarterly results of a financial institution (for example, customer reviews today), then as these short bursts of words are difficult to analyze, i think the results are dramatic if implemented correctly.

Another technique is the Hidden Markov model or (HMM). This model uses sequential data that can predict words to complete a sentence for that matter or an algorithm that recognizes characters on a road sign. An example of this model is used for autonomous driving where the autonomous car recognizes the letters on the traffic sign, or a variation of them. The time will come when only driverless vehicles will be allowed in city centers within a mile radius, and if a human is found driving within that mile radius they will be reprimanded … except for another. job.

Another technique that has dominated NLP since 2015 is neural networks. It is developing rapidly, including sentiment analysis, which you can find in word and phrase shape recognition, machine translation, text generation, and text classification. Neural networks are made up of hidden layers used to weigh and process information by performing calculations to make sense of the data. These hidden layers come in various forms.

These are the most common techniques, but one will surely find many other use cases for the treatment of NLP that have already been discovered, such as identifying major turning points in a novel. Is this book a page turner, for example. Or you can experience instant character analysis and so on. Another example will be script writing. Producers are always concerned about the budget, so an algorithm powered by NLP will have the ability to understand the costs of a scene and make recommendations for a more profitable budget.

I mentioned earlier that companies are already very successful in implementing the five stages of AI including healthy robotics and computer vision in the movie. But implementation for small and medium-sized businesses becomes a much bigger challenge. Resources and talent are two common constraints besides budget.

Here are the factors you can take into account when trying to implement NLP in your SME organization:

Variation in scope and complexity: NLP projects vary wildly in scope and complexity, ranging from a few hours of lonely work setting up a chatbot to the highly complex efforts of a skilled team to analyze and synthesize groups of words with unlimited computing resources for the datasets. structured and unstructured.

Performance variations: The performance of a given AI model can vary considerably depending on the type of language to which it is applied, for example, customer reviews, contracts, medical records, scientific publications, patents, legal texts, long texts (like books) very short texts (like Tweets). The language used to train the algorithm must be the same as that of the text to which it will be applied. For example, an NLP algorithm that was trained on Tweets would perform poorly if applied to medical records.

Labeling data: It’s primordial. Determine if part of the process requires humans to create labels for your data, and budget accordingly. Tags are needed to perform supervised training, whereby an algorithm learns from the training dataset based on a predefined outcome. Collecting, organizing and labeling data is essential before embarking on an NLP path.

Scalability is another implementation factor. Algorithms and services will not work the same in all languages ​​and may or may not work at all for others.

Examples of NLP used at mass scales now include:

• Chatbots, such as virtual assistants and customer support chatbots
• Machine translation (for example, translating a website or restaurant menu from Mandarin to English)
• Autocomplete and autocorrect on smartphones
• Extracting information from websites to optimize search engine results
• Classification of emails, such as the distinction between spam and legitimate emails
• Improved customer service by analyzing recorded customer calls
• Sentiment analysis of customer reviews is widely used in surveys.

For SMEs, these are some of the challenges of NLP implementation. In a follow-up article, we’ll discuss implementing Computer Vision for your business. Large companies with unlimited resources like Tesla have already successfully implemented CV, but it is quite a challenge for small businesses.