There are numerous areas for future work, such as: using verb dictionaries as semi-supervised seeds or priors; interactive learning between political science researchers and unsupervised algorithms; building low-dimensional scaling, or hierarchical structure, into the model; and learning the actor lists to handle changing real-world situations and new domains.
adding more supervision to the model will be crucial to improve semantic quality and make it useful for researchers.