Marco Pennacchiotti

Data-driven political science

The tutorial will summarize the state-of-the art in the growing area of computational political science. Like many others, this research domain is being revolutionized by the availability of open, big data and the increasing reach and importance of social media. The surging interest on the part of the academic community is matched by intense efforts on the part of political campaigns to use online data in order to learn how to best disseminate information and reach the right potential donors or voters. In this context, a tutorial can summarize existing methods in a fascinating, high-interest area and allow participants with diverse backgrounds to get inspiration from the methods and problems studied. The tutorial will feature seminal research concerning (i) political polarization, (ii) election prediction and polling, and (iii) political campaigning and influence propagation. The goal is not only to familiarize attendees with ideas from related conferences such as WWW, ICWSM or CIKM, but also to present ideas and quantitative methods closer to political science such as Pooles and Rosenthals NOMINATE score for a politicians political orientation.

FATE: Annotating a Textual Entailment Corpus with FrameNet

Several works show that predicate-argument structure is a level of analysis relevant for addressing Natural Language Processing problems, such as Textual Entailment (another study on Textual Entailment can be found in this volume). Although large resources like FrameNet are available (see also the chapter on FrameNet in this volume), attempts to integrate this type of information into a system for textual entailment has not delivered the expected gain in performance. The reasons for this result are not fully obvious; candidates include FrameNet’s restricted coverage, limitations of semantic parsers, or insufficient modeling of FrameNet information. To enable further insight on this issue, in this paper we present FATE (FrameNet-Annotated Textual Entailment), a manually built, fully reliable frame-annotated RTE corpus. The annotation covers the 800 pairs of the RTE-2 test set. This dataset offers a safe basis for RTE systems to experiment, and enables researchers to develop clearer ideas on how to integrate frame knowledge effectively into semantic inference tasks like recognizing textual entailment. We describe and present statistics over the adopted annotation, which introduces a new schema based on full-text annotation of so called relevant frame-evoking elements. (This chapter is based on Burchardt, Pennacchiotti, Proceedings of the sixth international conference on language resources and evaluation (LREC’08) (2008) [7].)