<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-1016">
  <Title>Modeling Commonality among Related Classes in Relation Extraction</Title>
  <Section position="4" start_page="121" end_page="121" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> The relation extraction task was formulated at MUC-7(1998). With the increasing popularity of ACE, this task is starting to attract more and more researchers within the natural language processing and machine learning communities.</Paragraph>
    <Paragraph position="1"> Typical works include Miller et al (2000), Zelenko et al (2003), Culotta and Sorensen (2004), Bunescu and Mooney (2005a), Bunescu and Mooney (2005b), Zhang et al (2005), Roth and Yih (2002), Kambhatla (2004), Zhao and Grisman (2005) and Zhou et al (2005).</Paragraph>
    <Paragraph position="2"> Miller et al (2000) augmented syntactic full parse trees with semantic information of entities and relations, and built generative models to integrate various tasks such as POS tagging, named entity recognition, template element extraction and relation extraction. The problem is that such integration may impose big challenges, e.g. the need of a large annotated corpus. To overcome the data sparseness problem, generative models typically applied some smoothing techniques to integrate different scales of contexts in parameter estimation, e.g. the back-off approach in Miller et al (2000).</Paragraph>
    <Paragraph position="3"> Zelenko et al (2003) proposed extracting relations by computing kernel functions between parse trees. Culotta and Sorensen (2004) extended this work to estimate kernel functions between augmented dependency trees and achieved F-measure of 45.8 on the 5 relation types in the</Paragraph>
  </Section>
class="xml-element"></Paper>