Difference between revisions of "S. Patwardhan and E. Riloff. EMNLP 2009"
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its local context, the authors used [[UsesMethod::Naive Bayes classifier]]. | its local context, the authors used [[UsesMethod::Naive Bayes classifier]]. | ||
The features include lexical matches, semantic features, and syntactic relations. | The features include lexical matches, semantic features, and syntactic relations. | ||
| + | |||
| + | the MUC-4 terrorism corpus and ProMed disease outbreaks corpus | ||
| + | For the event that often discussed later in a document, far | ||
| + | removed from the main event description, | ||
| + | sentential event recognizer tends to generate low | ||
| + | probabilities for such sentences | ||
Revision as of 04:36, 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. Named entities, lexico-syntactic pattern features, sentence length, bag of words, and verb tense are used as features.
To determine whether a noun phrase can be a legitimate filler for a specific type of event role based on its local context, the authors used Naive Bayes classifier. The features include lexical matches, semantic features, and syntactic relations.
the MUC-4 terrorism corpus and ProMed disease outbreaks corpus For the event that often discussed later in a document, far removed from the main event description, sentential event recognizer tends to generate low probabilities for such sentences
