Currently, there are situations where queries cannot be specified easily: by analysts looking at unfamiliar material and by clients who struggle to define their interests. Reinforcement learning, combined with relevance feedback, will allow for these issues to be resolved using an interactive search process. Results from the model can be extracted for integration into automated pipelines.
There should be additional support for users making relevance judgements. Documents written for a particular audience can involve implicit details that are hard for casual readers to detect (newspapers or social media from a specific political or cultural context, for example). This can involve subtle differences in word usage that alter the semantic meaning of certain terms. By augmenting documents with techniques based on word embeddings and language models, we can highlight such semantic shifts to make analysts aware of these differences.