The project aims to expose advanced text-mining and language understanding techniques such that they can be used by domain experts who do not have a computer science, programming, linguistics or machine-learning background.
This effort is two pronged: on the one hand, building the right software and abstraction layers such that existing state-of-the-art techniques are available without requiring technical background, and on the other hand identifying missing pieces in current language-processing and automatic text understanding techniques that can and should be improved, and improving them.
Providing domain experts (e.g. experts in biology, chemistry, political sciences, etc) access to text-mining techniques is of paramount importance to society: the amount of text in the world, especially in scientific and legal domains, is growing in an incredible pace, and it is currently nearly impossible for such experts to stay on top of the literature beyond their narrow field of expertise. Using text-mining and automatic text processing tools, can help these scientists, researchers and policy-makers tame the amounts of text and extract useful information from them.
When this project started, systems like ChatGPT were not available, and the initial phases developed systems that worked in the pre-GPT world. These systems proved to be very effective also with the post-GPT world and obtaining results which AI chatbots cannot match, but user's expectations (including expert users expectations) regarding how "intelligent" systems can and should behave, and especially how they as users should interact with these systems: while pre-GPT users were willing to invest in learning to operate specialized systems to solve their needs, post-GPT they expect to be able to communicate freely via natural language and the system to "understand what they want and just work". GPT-like systems allow us to do much more in terms language understanding and text-mining, but users also evolved to demand much more---more than what the technology can provide.
Thus, in the second half of the project duration, we adapted our objectives to investigating how we can accomodate the initial objective (exposing advanced text-mining techniques to domain experts) in a post-GPT world: how can we most effectively use the new technology to bridge the gap between general-purpose language-based AI systems, and large and specialised text collections, while remaining accurate and trustworthy.
