Miltsakaki, Eleni

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Now showing 1 - 3 of 3
  • Publication
    The Penn Discourse Treebank 2.0 Annotation Manual
    (2007-12-17) Prasad, Rashmi; Miltsakaki, Eleni; Dinesh, Nikhil; Lee, Alan; Joshi, Aravind; Robaldo, Livio; Webber, Bonnie L
    This report contains the guidelines for the annotation of discourse relations in the Penn Discourse Treebank (, 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.
  • Publication
    Movie/Script: Alignment and Parsing of Video and Text Transcription
    (2008-10-01) Cour, Timothee; Jordan, Chris; Miltsakaki, Eleni; Taskar, Ben
    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.
  • Publication
    Sense Annotation in the Penn Discourse Treebank
    (2008-02-10) Miltsakaki, Eleni; Lee, Alan; Joshi, Aravind K; Robaldo, Livio
    An important aspect of discourse understanding and generation involves the recognition and processing of discourse relations. These are conveyed by discourse connectives, i.e., lexical items like because and as a result or implicit connectives expressing an inferred discourse relation. The Penn Discourse TreeBank (PDTB) provides annotations of the argument structure, attribution and semantics of discourse connectives. In this paper, we provide the rationale of the tagset, detailed descriptions of the senses with corpus examples, simple semantic definitions of each type of sense tags as well as informal descriptions of the inferences allowed at each level.