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Unformatted text preview: on problem for the labels of each word,
with the surrounding words as input feature vector. A frequent way of forming
the feature vector is a binary encoding scheme. Each feature component can be
considered as a test that asserts whether a certain pattern occurs at a speciﬁc
position or not. For example, a feature component takes the value 1 if the
previous word is the word "John" and 0 otherwise. Of course we may not only
test the presence of speciﬁc words but also whether the words starts with a
capital letter, has a speciﬁc sufﬁx or is a speciﬁc part-of-speech. In this way
results of previous analysis may be used.
Now we may employ any efﬁcient classiﬁcation method to classify the word
labels using the input feature vector. A good candidate is the Support Vector
Machine because of its ability to handle large sparse feature vectors efﬁciently.
Takeuchi & Collier (2002) used it to extract entities in the molecular biology
3.3.2 Hidden Markov Models One problem of standard classiﬁcation approaches is that...
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- Summer '11