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<Paper uid="W05-0611">
  <Title>Improving sequence segmentation learning by predicting trigrams</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Symbolic machine-learning classifiers are known to suffer from near-sightedness when performing sequence segmentation (chunking) tasks in natural language processing: without special architectural additions they are oblivious of the decisions they made earlier when making new ones.</Paragraph>
    <Paragraph position="1"> We introduce a new pointwise-prediction single-classifier method that predicts tri-grams of class labels on the basis of windowed input sequences, and uses a simple voting mechanism to decide on the labels in the final output sequence. We apply the method to maximum-entropy, sparsewinnow, and memory-based classifiers using three different sentence-level chunking tasks, and show that the method is able to boost generalization performance in most experiments, attaining error reductions of up to 51%. We compare and combine the method with two known alternative methods to combat near-sightedness, viz. a feedback-loop method and a stacking method, using the memory-based classifier. The combination with a feedback loop suffers from the label bias problem, while the combination with a stacking method produces the best overall results.</Paragraph>
  </Section>
class="xml-element"></Paper>