Corpus-driven Semantics of Concession: Where do Expectations Come from?

                                                                                              Concession is one of the trickiest semantic discourse relations appearing in natural language. Many have tried to sub-categorize Concession and to define formal criteria to both distinguish its subtypes as well as for distinguishing Concession from the (similar) semantic relation of Contrast. But there is still a lack of consensus among the different proposals. In this paper, we focus on those approaches, e.g. (Lagerwerf 1998), (Winter & Rimon 1994), and (Korbayova & Webber 2007), assuming that Concession features two primary interpretations, "direct" and "indirect". We argue that this two way classification falls short of accounting for the full range of variants identified in naturally occurring data. Our investigation of one thousand Concession tokens in the Penn Discourse Treebank (PDTB) reveals that the interpretation of concessive relations varies according to the source of expectation. Four sources of expectation are identified. Each is characterized by a different relation holding between the eventuality that raises the expectation and the eventuality describing the expectation. We report a) a reliable inter-annotator agreement on the four types of sources identified in the PDTB data, b) a significant improvement on the annotation of previous disagreements on Concession-Contrast in the PDTB and c) a novel logical account of Concession using basic constructs from Hobbs' (1998) logic. Our proposal offers a uniform framework for the interpretation of Concession while accounting for the different sources of expectation by modifying a single predicate in the proposed formulae

Movie/Script: Alignment and Parsing of Video and Text Transcription

Movies and TV are a rich source of diverse and complex video of people, objects, actions and locales “in the wild”. Harvesting automatically labeled sequences of actions from video would enable creation of large-scale and highly-varied datasets. To enable such collection, we focus on the task of recovering scene structure in movies and TV series for object tracking and action retrieval. We present a weakly supervised algorithm that uses the screenplay and closed captions to parse a movie into a hierarchy of shots and scenes. Scene boundaries in the movie are aligned with screenplay scene labels and shots are reordered into a sequence of long continuous tracks or threads which allow for more accurate tracking of people, actions and objects. Scene segmentation, alignment, and shot threading are formulated as inference in a unified generative model and a novel hierarchical dynamic programming algorithm that can handle alignment and jump-limited reorderings in linear time is presented. We present quantitative and qualitative results on movie alignment and parsing, and use the recovered structure to improve character naming and retrieval of common actions in several episodes of popular TV series

The Penn Discourse Treebank 2.0 Annotation Manual

This report contains the guidelines for the annotation of discourse relations in the Penn Discourse Treebank (http://www.seas.upenn.edu/~pdtb), PDTB. Discourse relations in the PDTB are annotated in a bottom up fashion, and capture both lexically realized relations as well as implicit relations. Guidelines in this report are provided for all aspects of the annotation, including annotation explicit discourse connectives, implicit relations, arguments of relations, senses of relations, and the attribution of relations and their arguments. The report also provides descriptions of the annotation format representation