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In the concluding part V (Outlook), based on a survey conducted among the contributing authors, the prospects of the research field of anaphora processing are discussed, and promising new areas of interdisciplinary cooperation and emerging application scenarios are identified. Part IV (Applications) deals with a selection of important anaphora and coreference resolution applications, discussing particular scenarios in diverse domains and distilling a best-practice model for systematically approaching new application cases. Part III (Algorithms) provides a thorough description of state-of-the-art anaphora resolution algorithms, covering enhanced machine learning methods as well as techniques for accomplishing important subtasks such as mention detection and acquisition of relevant knowledge. Acknowledging the central importance of shared resources, part II (Resources) covers annotated corpora, formal evaluation, preprocessing technology, and off-the-shelf anaphora resolution systems. In part I (Background), it provides a general introduction, which succinctly summarizes the linguistic, cognitive, and computational foundations of anaphora processing and the key classical rule- and machine-learning-based anaphora resolution algorithms.
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The book offers an overview of recent research advances, focusing on practical, operational approaches and their applications. Anaphora and coreference resolution both refer to the process of linking textual phrases (and, consequently, the information attached to them) within as well as across sentence boundaries, and to the same discourse referent. Build a source dist with python3 setup.py clean & rm dist/* & python3 setup.This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to today’s state-of-the-art approaches, which use advanced empirically grounded techniques, automatic knowledge acquisition, and refined linguistic modeling to make a real difference in real-world applications.
#Shortcuts creating checklist from taskpaper file update#
Update the changelog, link the versions.If a header has a comment, the previous task or project is going to end up.The TaskPaper file must start with a project.In both cases, Pythonista will prompt you for the placeholder values if there
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Here's the result of parsing the template above. If there are any placeholders, you'll be prompted for Last minute: Set thermostat to travel mode Water plants import the template on the Mac, pass the TaskPaper filename to the Find good place for dinner forget to check NYT's "36 hours in. Here is a packing list template with 3 placeholders: Prepare luggage for trip to $destination: $due $destination A task under a task is a checklist item.Tasks under it can be indented or not, which is more flexible than the A project under a project is a considered a heading.The and tags expect a value and will be used for the "when"Īnd "deadline" attributes of a project or task.Placeholders must be defined on the second line of the file.The indentation must be done with Tabs, which is the normal TaskPaper behavior.Popthings supports placeholder replacement, headers and checklist items.ĭate parsing is done by Things itself, so anything Things supports is also SHORTCUT variable in the Examples/Widget/Launcher.py file:, Usage To add a button on the Today Widget, use the following entry for the The Share Extension shortcut, add the popthings.py script asĪ new shortcut in the Pythonista settings. If you want to use the Share Sheet Extension.
#Shortcuts creating checklist from taskpaper file download#
Download the popthings.pyįile to the "This iPhone/iPad" location. Both Python 2 and Python 3 are supported.
#Shortcuts creating checklist from taskpaper file code#
Into Things by Cultured Code Installation On macOS IOS and command line tool to import a TaskPaper template with placeholders
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