tailieunhanh - Báo cáo khoa học: "Learning Script Knowledge with Web Experiments"

We describe a novel approach to unsupervised learning of the events that make up a script, along with constraints on their temporal ordering. We collect naturallanguage descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph representation of the script’s temporal structure using a multiple sequence alignment algorithm. The evaluation of our system shows that we outperform two informed baselines. | Learning Script Knowledge with Web Experiments Michaela Regneri Alexander Koller Manfred Pinkal Department of Computational Linguistics and Cluster of Excellence Saarland University Saarbrucken regneri koller pinkal @ Abstract We describe a novel approach to unsupervised learning of the events that make up a script along with constraints on their temporal ordering. We collect naturallanguage descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph representation of the script s temporal structure using a multiple sequence alignment algorithm. The evaluation of our system shows that we outperform two informed baselines. 1 Introduction A script is a standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor Barr and Feigenbaum 1981 . Scripts are fundamental pieces of commonsense knowledge that are shared between the different members of the same culture and thus a speaker assumes them to be tacitly understood by a hearer when a scenario related to a script is evoked When one person says I m going shopping it is an acceptable reply to say did you bring enough money because the SHOPPING script involves a payment event which again involves the transfer of money. It has long been recognized that text understanding systems would benefit from the implicit information represented by a script Cullingford 1977 Mueller 2004 Miikkulainen 1995 . There are many other potential applications including automated storytelling Swanson and Gordon 2008 anaphora resolution McTear 1987 and information extraction Rau et al. 1989 . However it is also commonly accepted that the large-scale manual formalization of scripts is infeasible. While there have been a few attempts at doing this Mueller 1998 Gordon 2001 efforts in which expert annotators create script knowledge bases clearly don t scale. The same holds true of the script-like structures called