Learning Script Knowledge with Web Experiments

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 979–988, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Learning Script Knowledge with Web Experiments Michaela Regneri Alexander Koller Department of Computational Linguistics and Cluster of Excellence Saarland University, Saarbr¨ucken {regneri|koller|pinkal}@coli.uni-saarland.de Manfred Pinkal Abstract We describe a novel approach to unsuper- vised learning of the events that make up a script, along with constraints on their temporal ordering. We collect natural- language descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph rep- resentation of the script’s temporal struc- ture 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 cul- ture, and thus a speaker assumes them to be tac- itly understood by a hearer when a scenario re- lated 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 under- standing systems would benefit from the implicit information represented by a script (Cullingford, 1977; Mueller, 2004; Miikkulainen, 1995). There are many other potential applications, includ- ing 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 in- feasible. 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 “scenario frames” in FrameNet (Baker et al., 1998). There has recently been a surge of interest in automatically learning script-like knowledge re- sources from corpora (Chambers and Jurafsky, 2008b; Manshadi et al., 2008); but while these efforts have achieved impressive results, they are limited by the very fact that a lot of scripts – such as SHOPPING – are shared implicit knowledge, and their events are therefore rarely elaborated in text. In this paper, we propose a different approach to the unsupervised learning of script-like knowl- edge. We focus on the temporal event structure of scripts; that is, we aim to learn what phrases can describe the same event in a script, and what con- straints must hold on the temporal order in which these events occur. We approach this problem by asking non-experts to describe typical event se- quences in a given scenario over the Internet. This allows us to assemble large and varied collections of event sequence descriptions (ESDs), which are focused on a single scenario. We then compute a temporal …

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