Information Extraction ( IE ) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it . Often , however , role fillers occur in clauses that are not directly linked to an event word . We present a new model for event extraction that jointly considers both the local context around a phrase along with the wider sentential context in a probabilistic framework . Our approach uses a sentential event recognizer and a plausible role-filler recognizer that is conditioned on event sentences . We evaluate our system on two IE data sets and show that our model performs well in comparison to existing IE systems that rely on local phrasal context . Information Extraction ( IE ) systems typically use extraction patterns ( e.g. , Soderland et al . ( 1995 ) , Riloff ( 1996 ) , Yangarber et al . ( 2000 ) , Califf and Mooney ( 2003 ) ) or classifiers ( e.g. , Freitag ( 1998 ) , Freitag and McCallum ( 2000 ) , Chieu et al . ( 2003 ) , Bunescu and Mooney ( 2004 ) ) to extract role fillers for events . Most IE systems consider only the immediate context surrounding a phrase when deciding whether to extract it . For tasks such as named entity recognition , immediate context is usually sufficient . But for more complex tasks , such as event extraction , a larger field of view is often needed to understand how facts tie together . Most IE systems are designed to identify role fillers that appear as arguments to event verbs or nouns , either explicitly via syntactic relations or implicitly via proximity ( e.g. , John murdered Tom or the murder of Tom by John ) . But many facts are presented in clauses that do not contain event words , requiring discourse relations or deep structural analysis to associate the facts with event roles . For example , consider the sentences below : Seven people have died . . . and 30 were injured in India after terrorists launched an attack on the Taj Hotel . . . . in Mexico City and its surrounding suburbs in a Swine Flu outbreak . . . . after a tractor-trailer collided with a bus in Arkansas . Two bridges were destroyed . . . in Baghdad last night in a resurgence of bomb attacks in the capital city . . . . and $ 50 million in damage was caused by a hurricane that hit Miami on Friday . . . . to make way for modern , safer bridges that will be constructed early next year . These examples illustrate a common phenomenon in text where information is not explicitly stated as filling an event role , but readers have no trouble making this inference . The role fillers above ( seven people , two bridges ) occur as arguments to verbs that reveal state information ( death , destruction ) but are not event-specific ( i.e. , death and destruction can result from a wide variety of incident types ) . IE systems often fail to extract these role fillers because these systems do not recognize the immediate context as being relevant to the specific type of event that they are looking for . We propose a new model for information extraction that incorporates both phrasal and sentential evidence in a unified framework . Our unified probabilistic model , called GLACIER , consists of two components : a model for sentential event recognition and a model for recognizing plausible role fillers . The Sentential Event Recognizer offers a probabilistic assessment of whether a sentence is discussing a domain-relevant event . The Plausible Role-Filler Recognizer is then conditioned to identify phrases as role fillers based upon the assumption that the surrounding context is discussing a relevant event . This unified probabilistic model allows the two components to jointly make decisions based upon both the local evidence surrounding each phrase and the " peripheral vision " afforded by the sentential event recognizer . This paper is organized as follows . Section 2 positions our research with respect to related work . Section 3 presents our unified probabilistic model for information extraction . Section 4 shows experimental results on two IE data sets , and Section 5 discusses directions for future work . We presented a unified model for IE that balances the influence of sentential context with local contextual evidence to improve the performance of event-based IE . Our experimental results showed that using sentential contexts indeed produced better results on two IE data sets . Our current model uses supervised learning , so one direction for future work is to adapt the model for weakly supervised learning . We also plan to incorporate discourse features and investigate even wider contexts to capture broader discourse effects .

Challenge: Information extraction systems cannot extract role filters which are arguments to verbs but are not event-specific because they do not recognize the contexts as relevant.
Approach: They propose a unified probabilistic model composed of sentential event recognition and plausible role filter recognition models which run both on phrasal and sentence levels.
Outcome: The proposed unified model outperforms the existing systems that rely on the local phrasal context of two information extraction datasets.