Show simple item record

dc.contributor.authorLilian Wanzare, Alessandra Zarcone, Stefan Thater, Manfred Pinkal
dc.date.accessioned2020-11-23T09:00:59Z
dc.date.available2020-11-23T09:00:59Z
dc.date.issued2017
dc.identifier.urihttps://repository.maseno.ac.ke/handle/123456789/2902
dc.description.abstractWe present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets (representing event types) and inducing their temporal order. Our model exploits semantic and positional similarity and allows for flexible event order, thus overcoming the rigidity of previous approaches. We incorporate crowdsourced alignments as prior knowledge and show that exploiting a small number of alignments results in a substantial improvement in cluster quality over state-of-the-art models and provides an appropriate basis for the induction of temporal order. We also show a coverage study to demonstrate the scalability of our approach.en_US
dc.publisherUniversitat des Saarlandesen_US
dc.titleInducing script structure from crowdsourced event descriptions via semi-supervised clusteringen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record