Projection: A Mixed-Initiative Research Process

Communication of dense information between humans and machines is relatively low bandwidth. Many modern search and recommender systems operate as machine learning black boxes, giving little insight as to how they represent information or why they take certain actions. We present Projection, a mixed-initiative interface that aims to increase the bandwidth of communication between humans and machines throughout the research process. The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. Potential customers have shown interest in the application integrating their research outlining and search processes, enabling them to structure their searches in hierarchies, and helping them visualize related spaces of knowledge.

Exploratory Search with Sentence Embeddings

Exploratory search aims to guide users through a corpus rather than pinpointing exact information. We propose an exploratory search system based on hierarchical clusters and document summaries using sentence embeddings. With sentence embeddings, we represent documents as the mean of their embedded sentences, extract summaries containing sentences close to this document representation and extract keyphrases close to the document representation. To evaluate our search system, we scrape our personal search history over the past year and report our experience with the system. We then discuss motivating use cases of an exploratory search system of this nature and conclude with possible directions of future work.