@conference {wenskovitch2018effect,
	title = {The Effect of Semantic Interaction on Foraging in Text Analysis},
	booktitle = {2018 IEEE Conference on Visual Analytics Science and Technology (VAST)},
	year = {2018},
	abstract = {Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users{\textquoteright} semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), "semantic interaction foraging" (SIF) occurs as a result of the user{\textquoteright}s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26\% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.},
	author = {Wenskovitch, John and Lauren Bradel and Michelle Dowling and House, Leanna and North, Chris}
}
@conference {Wang2014a,
	title = {Event-Based Text Visual Analytics},
	booktitle = {VAST Challenge 2014},
	year = {2014},
	address = {Paris, France},
	abstract = {We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge 1 (MC1). A combination of student-created and commericially available software are used to solve various aspects of the scenario. In addition to applying entitiy extraction and topic modelling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to augment the data spatialization and determine what information should be presented and suggested to the user. Additionally, we visualize extracted topics using Tableau to construct a timeline of events surrounding the questions posed by the challen},
	keywords = {event extraction, Semantic interaction, sensemaking, topic modelling},
	author = {Wang, Ji and Lauren Bradel and North, Chris}
}
