Difference between revisions of "S. Patwardhan and E. Riloff. EMNLP 2009"
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such as event extraction, a larger field of view is | such as event extraction, a larger field of view is | ||
often needed to understand how facts tie together. | often needed to understand how facts tie together. | ||
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This paper proposed a new model for event extraction. | This paper proposed a new model for event extraction. | ||
To determine whether a noun phrase should be extracted as a filler for an event role | To determine whether a noun phrase should be extracted as a filler for an event role | ||
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# appears in an event sentence, and | # appears in an event sentence, and | ||
# is a legitimate filler for the event role. | # is a legitimate filler for the event role. | ||
+ | To compute the probability of a sentence describing a relevant | ||
+ | event, they use SVM, which is not a probabilistic classifier. The authors | ||
+ | used the margin as an indicator of confidence. It worked well for them. |
Revision as of 04:07, 30 November 2010
Citation
S. Patwardhan and E. Riloff. A unified model of phrasal and sentential evidence for information extraction. in EMNLP 2009
Online version
Summary
Previous IE systems make decision only based on immediate context around a phrase. The authors argue that for more complex tasks, such as event extraction, a larger field of view is often needed to understand how facts tie together. This paper proposed a new model for event extraction. To determine whether a noun phrase should be extracted as a filler for an event role the new model computes the joint probability that NPi :
- appears in an event sentence, and
- is a legitimate filler for the event role.
To compute the probability of a sentence describing a relevant event, they use SVM, which is not a probabilistic classifier. The authors used the margin as an indicator of confidence. It worked well for them.