Normalized Alignment of Dependency Trees for Detecting Textual Entailment

In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information

Cost-based attribute selection for GRE (GRAPH-SC/GRAPH-FP)

In this paper we discuss several approaches to the problem of content determination for the generation of referring expressions (GRE) using the Graphbased framework of Krahmer et al. (2003). This work was carried out in the context of the First NLG Shared Task and Evaluation Challenge on Attribute Selection for Referring Expression Generation

Illustrating answers: an evaluation of automatically retrieved illustrations of answers to medical questions

In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the picture’s caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate the answer.In a user study, participants rated answer presentations consisting of a textual component and a picture. The textual component was a manually written reference answer; the picture was automatically retrieved by measuring the similarity between the text and either the picture’s caption or its section. The caption-based selection method resulted in more attractive presentations than the section-based method; the caption-based method was also more consistent in selecting informative pictures and showed a greater correlation between user-rated informativeness and the confidence of relevance of the system.When compared to manually selected pictures, we found that automatically selected pictures were rated similarly to decorative pictures, but worse than informative pictures

What Is to Be Learned from a Statewide Collection of PDFs

The Texas Digital Newspaper Program, operated by the University of North Texas Libraries, actively works to digitally preserve news in the form of print and born digital newspaper content via The Portal to Texas History. For two years, TDNP has partnered with the Texas Press Association to preserve born-digital newspaper titles from its member institutions. These PDF-based print masters total more than 3 million pages from over 500 titles across the state and allow UNT Libraries to explore significant metrics associated with born-digital newspaper content at a scale that previously had been impossible. This paper reports on exploratory investigations by the TDNP to understand aggregate patterns in the generation of born-digital news editions by analyzing technical metadata extracted from the 3 million pages currently in the preservation collection. While this research is still in its early stages, the goal is to provide an overview of current publishing practices of the more than 500 newspaper publishers across Texas. Furthermore, this research can enhance libraries’ understanding about current publishing trends as they plan digital preservation policies and practices in support of publisher preservation needs