@article{Li_Nenkova_2015, title={Fast and Accurate Prediction of Sentence Specificity}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9517}, DOI={10.1609/aaai.v29i1.9517}, abstractNote={ <p> Recent studies have demonstrated that specificity is an important characterization of texts potentially beneficial for a range of applications such as multi-document news summarization and analysis of science journalism. The feasibility of automatically predicting sentence specificity from a rich set of features has also been confirmed in prior work. In this paper we present a practical system for predicting sentence specificity which exploits only features that require minimum processing and is trained in a semi-supervised manner. Our system outperforms the state-of-the-art method for predicting sentence specificity and does not require part of speech tagging or syntactic parsing as the prior methods did. With the tool that we developed --- Speciteller --- we study the role of specificity in sentence simplification. We show that specificity is a useful indicator for finding sentences that need to be simplified and a useful objective for simplification, descriptive of the differences between original and simplified sentences. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Junyi and Nenkova, Ani}, year={2015}, month={Feb.} }