Project highlights

Built an AI-based Chrome extension that analyzes resumes and provides definitions of IT terms

Implemented a natural language processing algorithm to correctly extract critical data

Created our own tokenizer to speed up the analysis and enhance the accuracy

Chrome extension to automatically analyze and evaluate candidate CVs

The IT industry is developing at an insane pace. EJB, JSF, JPA, JBoss – new libraries and tools emerge every month, creating new tech terms. This makes candidate screening a complicated process – even for experienced recruiters, it becomes more and more difficult to keep all the technical terms in mind.

This is why GlossaryTech was built – the extension is designed to help recruitment managers with the resume screening process. Within seconds of analyzing a web page, this tool automatically sorts the technologies mentioned by a developer, highlights them in the resume, and provides their definitions. This significantly simplifies and speeds up the process for recruiters to evaluate whether a candidate is suitable for the role.

Implementing an NLP algorithm to correctly extract critical data

The main challenge in automatic text analysis is developing an algorithm capable of extracting crucial data and accurately interpreting it. Algorithms may miss important information because of unstructured documents and variations in meaning. With tech terms, the data analysis becomes even harder – for example, how do you define if the word "go" used in a developer's resume is a verb or a programming language?

To tackle these challenges, we developed an NLP algorithm that uses our tech glossary as a reference to precisely comprehend the meaning and context of language. By relying on machine learning, NLP can quickly identify the necessary information and categorize it correctly, thus expediting the resume screening process.

Building our own tokenizer for faster analysis of multi-word expressions

Although open-source libraries can work great in certain cases, they don’t allow full control over the analyzing process. To make GlossaryTech faster and more accurate in terms of data extraction, we decided to build our own tokenizer.

This tokenizer is designed for general purpose and works perfectly for the ICT domain. It is optimized for identifying particular entities or phrases, and it performs well not only with single words but also with multi-word phrases, such as "Ruby on Rails".