NLP as a subfield of AI is mostly applied for the tasks referring to recognizing, understanding, translating and creating human language models.
Everyone’s heard of ChatGPT and its skills to generate structured and comprehensive responses to complicated user queries. It’s also an example of NLP, based on LLM (Large Language model) system, and the spheres of NLP usage reach far beyond basic Chatbot functionality.
To understand how NLP solution can help your business, let’s have a look at NLP actual work step-by-step.
For example, we want to create an NLP model, that will be able to provide responses or useful insights from text analysis. The first step, no matter the type or sphere of NLP usage, lies in providing the amount of data that would allow the future NLP model to process them and provide comprehensive results. For instance, corporate documents or user requests will be a basis for future work. Silk Data can provide solutions that collect only the most vital document types, instead of hundreds of separated texts.
When the data collection is complete, texts must be prepared for analysis. This is called data preprocessing. Working with texts, this stage usually includes tokenization (when the text is divided into words), lemmatization and stemming (a process of reducing the word to its initial root form based both on logical and morphological analysis) and sentence segmentation (breaking the large text or abstract into sentences).
When the text and its parts are analyzed, they have to be presented in a form suitable for the machine to work with them. The process is called feature extraction and for the last decade it’s commonly based on word embeddings, when all the words and their combinations are represented in the lists of numbers (vectors). Through that, the model understands not only the spelling but also semantic meaning of the human language entities.
When the data are numerically represented, the process goes to the learning stage, when we train the NLP model. It implies statistical analysis of the transformed numerical data and learning their relations, patterns and structure with the further ability to optimize the model parameters. Understanding the relations between the data parts also allows the model to understand the meaning of the texts (for example, the grammar and stylistic structure or perform an entity analysis) we use on the initial stage. Worth noticing that proper model training can be most efficiently performed with large amounts of data.
The final stage is that the system generates required responses, predictions, insights or solutions in a human language form. The quality of the result, however, directly depends on the quality of all the previous steps.
This is how the NLP works transforming the raw text data into useful comprehensive insights. As we said, the variety of functions is numerous, so, working with texts, you can ask for information extraction, structuring, translation and many more.
Silk Data may offer a vast range of NLP services aimed for solving various business problems. Here are the most popular of them.
If you’re looking for marketing related workflow optimisation, Silk Data NLP development expertise can help. We already have a story of successful AI chatbot deployment, when the system was smoothly implemented into the client's publishing platform.
We also have a case of successful on-prem LLM (Large Language Model) deployment, when the solution we created was implemented into agency’s internal marketing platform to improve the agency client support.
Both solutions reduce the number of manual operations in the sphere of lead generation and customer support, while local implementation ensures data security and cost efficiency along with the higher level of control over the model’s configuration.
If you face an everyday struggle with analysing a huge number of documents, obtaining a well-trained NLP model will be a great advantage. Based on OCR and advanced document processing technologies, our solutions allow to optimize your work with texts, significantly reducing data collection and processing costs.
Your HR department can’t deal with hundreds of CVs arriving every week? We can provide a tool that will process them, finding the key words and qualifications needed, allowing your recruiters to quickly find the most suitable candidates.
Maybe, you find the process of contract preparation, review and signing long and painstaking? We’ve already implemented an AI solution that allows to identify the contract type and find the most important parts of it.
If you need to make research on any subject, you understand how much time the process of search analysis can take. This is also a task for NLP.
As an example, we’ve successfully implemented tool for automated news article analysis, providing a Chrome browser extension. It allows to compare the chosen news article to the related publications all around the Internet, clustering the semantically similar articles and classifying them according to the same patterns and key points.
It’s very useful for content writers, publishers and journalists who now could quickly search through the topic, extracting the most valuable information.
The traditional keyword-based search practice can be inefficient in some cases. Users have more and more need in advanced system that will take their search intensions into consideration.
For example, our AI-assisted search tool allows to improve your corporate searching processes through understanding and analysing documents content and semantics. Through that, you can get the most accurate, comprehensive and valuable data on your request.
Through years of work with different projects we’ve developed a system of NLP solutions that help to fulfil even the most complicated and challenging tasks.
Useful insights gaining, improved decision making and looking for new opportunities are the main tasks for modern marketing, but it requires hundreds of working hours and the participation of dozens of specialists. NLP makes it easier through advanced data analytics.
Advanced analytics based on Natural Language processing techniques and their abilities saves valuable time, sparing you from manual data analysis.
Working with a great number of documents is a one more problem that devour your time, inhibit the workflow of your team. Whether it’s knowledge base management or plagiarism checking, your professionals spend hours of their time every single day to deal with monotonous operations. We offer a solution based on ML and NLP algorithms; an AI text compare tool with customisable processing criteria.
For example, you can compare two documents, and the system shows the differences between them, which is very useful when you deal with terms and policy changes. You can also compare two different texts and see the parts which are semantically identical to one another, so the plagiarism detection becomes a task of mere minutes.
Many spheres, such as education, require working with a great number of textual data — articles, reviews or the whole books. Reading all these materials and getting the most valuable parts is a complicated task, and some people can spend hours on one article and still don’t comprehend it.
A one more task for NLP. Our text summarisation solution uses the most up-to-date AI algorithms to ‘read’ the text you need and extract the data comprehensive enough to cover its topic and context.
At the end, a text of 2, 3 or 5 thousand words can be reduced to 200–300 hundred words article, making it easier to understand and work with.
NLP’s abilities allow to process and analyse large amounts of textual data (reports, reviews, statistics) and extract and summarise the most valuable information. This ability is very useful for marketing and publishing industries, as it accelerates and improves the process of naming, user experience analysis and further strategies planning.
NLP-based chatbots can successfully perform standard customer support operations, replying to users’ questions, providing the information needed and reducing the need of human intervention to minimum.
NLP usage leads to automation of many processes, from student assignments checking in education to resume viewing and contract or application analysis in recruiting and Fintech. It reduces the overload of organisation, allowing the specialists to focus on other important issues.
Manual work often leads to mistakes which can be crucial, especially for such spheres as banking or legal. Properly trained NLP model allows to reduce the number of mistakes, related to human inattentiveness or tiredness to minimum.
Though every project requires its unique planning, development, testing and deployment stages, Silk Data has created a rather stable system of rules and concepts that allow us to provide the best possible solutions.
First and one of the most important parts of any NLP projects is the stage of analysis. Our work is dedicated to analysis of the data and requests you provide, both from technical and business points. It provides a clear understanding, both for us and the customer, whether their idea and resources can lead to any significant results and if the outcomes cover the costs.
We want to preserve you from unnecessary money and time losses, so, if you’re not sure that the result will fulfil your business needs, we offer the possibility to test the concept. That’s why you may ask for PoC (Proof of Concept) or MVP (Minimal Valuable Product) development to see the real demonstration of basic functionality of the future complete solution.
At Silk Data we follow strict policy, referring the clients’ personal and corporate data. All our NLP solutions correspond all legal and business regulations and are built with additional security features such as access management, authentication and data encryption.
"Our NLP development team comprehend both practical and academic expertise in creating unique solutions that cover particular customer needs. Led by PhD experts, our developers create products based on comprehensive scientific, technological and business analysis, built with the help of the most efficient and up-to-date technologies and practices."
Yuri Svirid, PhD. — CEO Silk Data
It’s really hard to name exact numbers, as NLP development process depends on many factors, including client’s needs, the amount of initial data required for NLP model training and the necessity to implement the solution within the client internal working infrastructure. However, there’re still some cost frames of NLP development.
Though any AI project can’t be preserved from unexpected difficulties, the most common challenges we can face is the improper initial data amount and quality. This aspect is crucial for the NLP model training as it influences the quality of the data processing and analysis results.
Indeed. ChatGPT is one of the most advanced and well-known NLP models, that can solve the tasks of text understanding and generation with additional deep learning features.
