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  • ConvoKit 4. 1. 0 documentation
    ConvoKit: Conversational Analysis Toolkit ¶ This toolkit contains tools to extract conversational features and analyze social phenomena in conversations, using a single unified interface inspired by (and compatible with) scikit-learn Several large conversational datasets are included together with scripts exemplifying the use of the toolkit on these datasets The latest version is 4 1 0
  • Cornell Conversational Analysis Toolkit (ConvoKit) Documentation
    Cornell Conversational Analysis Toolkit (ConvoKit) Documentation This toolkit contains tools to extract conversational features and analyze social phenomena in conversations, using a single unified interface inspired by (and compatible with) scikit-learn
  • Installing ConvoKit — convokit 4. 1. 0 documentation
    By default, ConvoKit uses a native Python backend which keeps all data in memory during runtime This is suitable for most use cases and does not require any additional setup However, certain use cases (including low-memory environments and real-time applications) may prefer the alternative MongoDB backend, which requires additional setup
  • Datasets — convokit 4. 1. 0 documentation
    Conversations Gone Awry Dataset - Wikipedia version (CGA-WIKI) Dataset details Speaker-level information Utterance-level information Conversational-level information Usage Additional note Related links Contact Conversations Gone Awry Dataset - Reddit CMV version (CGA-CMV) Dataset details Speaker-level information Utterance-level information Conversational-level information Usage Contact
  • Corpus — convokit 4. 1. 0 documentation
    Corpus class convokit model corpus Corpus(filename: Optional [str] = None, utterances: Optional [List [convokit model utterance Utterance]] = None, db_collection_prefix: Optional [str] = None, db_host: Optional [str] = None, preload_vectors: List [str] = None, utterance_start_index: int = None, utterance_end_index: int = None, merge_lines: bool = False, exclude_utterance_meta: Optional [List
  • Examples — convokit 4. 1. 0 documentation
    Examples An index of useful examples to help you interactively explore ConvoKit’s features Be sure to take a look at the introductory tutorial before exploring these examples!
  • Core Concepts — convokit 4. 1. 0 documentation
    Core Concepts At the heart of ConvoKit are two key concepts: Corpora and Transformations At a high level, a Corpus represents a collection of one or more conversations, while a Transformation represents some action or computation you can do to a corpus To draw an analogy to language, corpora are the nouns of ConvoKit, and transformations are the verbs While this architecture may seem simple
  • Cornell Movie-Dialogs Corpus — convokit 4. 1. 0 documentation
    Cornell Movie-Dialogs Corpus A large metadata-rich collection of fictional conversations extracted from raw movie scripts (220,579 conversational exchanges between 10,292 pairs of movie characters in 617 movies) Distributed together with: Chameleons in Imagined Conversations: A new Approach to Understanding Coordination of Linguistic Style in Dialogs Cristian Danescu-Niculescu-Mizil and
  • Switchboard Dialog Act Corpus — convokit 4. 1. 0 documentation
    This is a Convokit-formatted version of the Switchboard Dialog Act Corpus (SwDA), originally distributed together with the following paper: Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Carol Van Ess-Dykema, and Marie Meteer





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