Paper: Towards Finding And Fixing Fragments: Using ML To Identify Non-Sentential Utterances And Their Antecedents In Multi-Party Dialogue

ACL ID P05-1031
Title Towards Finding And Fixing Fragments: Using ML To Identify Non-Sentential Utterances And Their Antecedents In Multi-Party Dialogue
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Non-sentential utterances (e.g. , short- answers as in “Who came to the party?”— “Peter.”) are pervasive in dialogue. As with other forms of ellipsis, the elided ma- terial is typically present in the context (e.g. , the question that a short answer an- swers). We present a machine learning approach to the novel task of identifying fragments and their antecedents in multi- party dialogue. We compare the perfor- mance of several learning algorithms, us- ing a mixture of structural and lexical fea- tures, and show that the task of identifying antecedents given a fragment can be learnt successfully (f(0.5) =.76); we discuss why the task of identifying fragments is harder (f(0.5) = .41) and finally report on a combined task (f(0.5) = .38).