id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_2dw3vuoprfdbdnrfirx5jj7jme	Ashutosh Modi	Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction	2017	15	.pdf	application/pdf	9358	872	61	Modi, A, Titov, I, Demberg, V, Sayeed, A & Pinkal, M 2017, 'Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction', Transactions of the Association for Computational Linguistics, vol. https://www.research.ed.ac.uk/portal/en/publications/modeling-semantic-expectation-using-script-knowledge-for-referent-prediction(01c319a9-4ee1-4b73-af5a-9ac0aad76603).html suggesting that human expectations at different levels of representation have separable effects on prediction and, as a consequence, that the modelling do not provide a computational model for estimating referent predictability. only structural linguistic features for predicting referents; the other uses general script-independent selectional preference features. We use the InScript corpus to develop computational models for the prediction of discourse referents (DRs) and to evaluate their prediction accuracy. to the Mechanical Turk experiment (Figure 2), our referent prediction model is asked to guess the upcoming DR. knowledge improves predictions of upcoming referents and that the script model is the best among We also used the estimated models to predict referring expression type	./cache/work_2dw3vuoprfdbdnrfirx5jj7jme.pdf	./txt/work_2dw3vuoprfdbdnrfirx5jj7jme.txt