Work

Improving event prediction by representing script participants

Year: 2016

Type: conference-paper

Abstract: Automatically learning script knowledge has proved difficult, with previous work not or just barely beating a most-frequent baseline.Script knowledge is a type of world knowledge which can however be useful for various task in NLP and psycholinguistic modelling.We here propose a model that includes participant information (i.e., knowledge about which participants are relevant for a script) and show, on the Dinners from Hell corpus as well as the InScript corpus, that this knowledge helps us to significantly improve prediction performance on the narrative cloze task.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n16-1067

Authors: Simon Ahrendt, Vera Demberg

Institution: Saarland University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Deutsche Forschungsgemeinschaft

Awards: G1951765764, G893193274