Natural Language Processing (NLP) has revolutionized the way we interact with technology. With the rise of machine learning (ML) and artificial intelligence (AI), NLP has become an essential tool for developers looking to create intelligent, intuitive applications. However, incorporating NLP models into an application stack has not always been an easy task. Fortunately, new tools are now making it easier than ever before. These tools are enabling developers to incorporate NLP models into their application stack with ease. Where previously, developers had to train NLP models from scratch, which was time-consuming and required specialized expertise, tools such as OpenAI and Hugging Face are making it easier to build a powerful set of ML features using pre-trained models that can be easily incorporated into any application stack. One of the biggest advantages of these new developer tools is that they make coding and complex queries more accessible, even to those without advanced coding expertise. With the use of pre-trained models, developers don’t need to start from scratch - they can simply use the models as a starting point and customize them to suit their needs. NLP models can usually be used 'out of the box' with some guidance for the model given in a 'prompt template' that provides context and response guidelines. This means that anyone can code and query, even the hard stuff, using simple, everyday language. How ML and NLP Play Together Unlike traditional rule-based systems, machine learning relies on a learning framework that allows computers to train themselves on input data. ML can use a wide range of models to process data, enabling it to understand both common and uncommon queries. Additionally, because machine learning models can continually improve from experience, they can handle edge cases independently without requiring manual reprogramming. NLP, on the other hand, is a type of technology that employs machine learning algorithms to enable machines to understand human communication. By leveraging large datasets, NLP can create tools that understand the syntax, semantics, and context of conversations. The Benefits of NLP Integration One of the main benefits of integrating NLP into an application stack is that it can speed up the development of business applications. By using pre-trained models, developers can save time on training and testing and can quickly incorporate NLP features into their applications and get them to market faster. Let’s start with task automation. NLP can automate tasks that would otherwise be time-consuming and costly. For example, the use of NLP can significantly speed up the process of analyzing large volumes of text data. Sentiment analysis, for instance, is a common NLP application that allows companies to quickly analyze customer reviews, social media posts, and other forms of user-generated content to identify patterns and trends. Another way that NLP can speed up development is by enabling developers to create more intuitive user interfaces. Natural language interfaces, such as chatbots and voice assistants, are becoming increasingly popular across a range of industries. These interfaces allow users to interact with applications using natural language, rather than having to navigate complex menus and user interfaces. By incorporating NLP into these interfaces, developers can create more intuitive and user-friendly applications. NLP can also speed up the process of content creation – one of the most time-consuming processes in many businesses, and one that requires significant human input and resources. However, with the use of NLP, developers can automate many aspects of content creation, such as content summarization, live transcripts and translations, and even the generation of new content. For example, a news outlet could use NLP to automatically generate summaries of news articles, allowing them to cover more stories in less time, or put out summaries across their social media channels. Perhaps one of the greatest value-adds from NLP is that it can help to speed up the process of data analysis and decision-making. By using NLP to analyze data, developers can quickly identify patterns and trends and make data-driven decisions in real-time. For instance, in the financial industry, NLP can be used to analyze market trends and predict stock prices, enabling traders to make informed investment decisions in real-time. Incorporating NLP Into an Existing Tech Stack It’s now easier than ever to incorporate NLP into an existing tech stack using new ML tools and frameworks such as OpenAI, Hugging Face, Spacy, or NLTK. It’s important to choose a tool that is well-documented, has a good track record, and has an active community of developers on hand to share knowledge and troubleshoot. Once a tool has been selected, it’s time to move on to data preprocessing, which involves cleaning, tokenizing, and stemming the text data to standardize it and make it readable by NLP algorithms. For instance, “stemming” is a technique that reduces words to their root form – instead of using the words "running," "ran," and "runner, the root form "run" can be used on its own. These techniques can help to reduce the size of the vocabulary and improve the accuracy of NLP models. Then it’s a case of selecting the right NLP model for a given use case. For instance, if a business is working on a sentiment analysis project, it might use pre-trained models such as BERT, GPT-2, or ULMFiT, which have already been trained on large volumes of conversational data. The benefits of integrating NLP into an application stack cannot be overstated and are key to creating intelligent, intuitive applications. Thanks to new tools, incorporating NLP capabilities into an existing tech stack is easier than ever, but there are still some important decisions to be made along the way, such as which tools and frameworks to use and which NLP models are most appropriate to achieve a company’s overall objectives. Many of these NLP models can be used “out of the box”, but in order to capitalize on the democratization of NLP technology, businesses need to lay the groundwork by ensuring their data is ready, and the right developer tools are deployed. Also published here
