Stereotypical character roles-also known as archetypes or dramatis personae-play an important function in narratives: they facilitate efficient communication with bundles of default characteristics and associations and ease understanding of those cha
racters' roles in the overall narrative. We present a fully unsupervised k-means clustering approach for learning stereotypical roles given only structural plot information. We demonstrate the technique on Vladimir Propp's structural theory of Russian folktales (captured in the extended ProppLearner corpus, with 46 tales), showing that our approach can induce six out of seven of Propp's dramatis personae with F1 measures of up to 0.70 (0.58 average), with an additional category for minor characters. We have explored various feature sets and variations of a cluster evaluation method. The best-performing feature set comprises plot functions, unigrams, tf-idf weights, and embeddings over coreference chain heads. Roles that are mentioned more often (Hero, Villain), or have clearly distinct plot patterns (Princess) are more strongly differentiated than less frequent or distinct roles (Dispatcher, Helper, Donor). Detailed error analysis suggests that the quality of the coreference chain and plot functions annotations are critical for this task. We provide all our data and code for reproducibility.
We present an interactive Plotting Agent, a system that enables users to directly manipulate plots using natural language instructions within an interactive programming environment. The Plotting Agent maps language to plot updates. We formulate this
problem as a slot-based task-oriented dialog problem, which we tackle with a sequence-to-sequence model. This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one. From this kind of feedback, we can then, in principle, continuously learn and improve the system. Given that plotting is widely used across data-driven fields, we believe our demonstration will be of interest to both practitioners such as data scientists broadly defined, and researchers interested in natural language interfaces.
This study tries to afford answers to a number of questions that arose concerning the
causes and the consequences of the war. What is conspiracy theory? Is there a conspiracy
on Syria? Where were the origins of conspiracy theory laid? What was the
role that
conspiracy played in the war on Syria?
Could the war on Syria really be just a result of the conspiracy or were there
additional eternal causes?
The study concludes that:1. War on Syria was an inevitability that most, if not all,
societies will arrive to.2. Creating “ Conspiracy theory” in Syria, is a Conspiracy itself. 3.
Believing in Conspiracy is a significant goal of conspiracy itself. 4. Supporting a
Conspiracy theory in Syria serves the great powers that invented the theory. 5. There is no
metaphysical roots of Conspiracy but historical causes founded in the social and economic
relations. 6. Regional and international powers put a further nail in the coffin of conspiracy
on Syria.
