Detecting Attribution Relations in Speech: a Corpus Study

Alessandra Cervone, Silvia Pareti, Peter Bell, Irina Prodanof, Tommaso Caselli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this work we present a methodology for the annotation of Attribution Relations (ARs) in speech which we apply to create a pilot corpus of spoken informal dialogues. This represents the first step towards the creation of a resource for the analysis of ARs in speech and the development of automatic extraction systems. Despite its relevance for speech recognition systems and spoken language understanding, the relation holding between quotations and opinions and their source has been studied and extracted only in written corpora, characterized by a formal register (news, literature, scientific articles). The shift to the informal register and to a spoken corpus widens our view of this relation and poses new challenges. Our hypothesis is that the decreased reliability of the linguistic cues found for written corpora in the fragmented structure of speech could be overcome by including prosodic clues in the system. The analysis of SARC confirms the hypothesis showing the crucial role played by the acoustic level in providing the missing lexical clues.
Original languageEnglish
Title of host publicationProc. Italian Conference on Computational Linguistics
Number of pages5
Publication statusPublished - 2014


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