<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-1017">
  <Title>Relation Extraction Using Label Propagation Based Semi-supervised Learning</Title>
  <Section position="5" start_page="134" end_page="135" type="concl">
    <SectionTitle>
5 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> This paper approaches the problem of semi-supervised relation extraction using a label propagation algorithm. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. In the classification process, the labels of unlabeled examples are determined not only by nearby labeled examples, but also by nearby unlabeled examples. Our experimental results demonstrated that this graph based algorithm can achieve better performance than SVM when only very few labeled examples are available, and also outperforms the bootstrapping method for relation extraction task.</Paragraph>
    <Paragraph position="1"> In the future, we would like to investigate more effective feature set or use feature selection to improve the performance of this graph-based semi-supervised relation extraction method.</Paragraph>
  </Section>
class="xml-element"></Paper>