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<Paper uid="W04-1202">
  <Title>Using Argumentation to Retrieve Articles with Similar Citations from MEDLINE</Title>
  <Section position="3" start_page="4" end_page="8" type="intro">
    <SectionTitle>
2 Background
</SectionTitle>
    <Paragraph position="0"> Digital libraries aim at structuring their records to facilitate user navigation. Interfaces visualizing  overlapping relationships of the standard library fields such as author and title in document collections are usually the most accessible to the user. Beyond these well-known targets, researchers (see de Bruijn and Martin, 2002, or Hirschman and al. 2002, for a survey) interested in information extraction and retrieval for biomedical applications have mostly focused on studying specific biological interactions (Stapley and Benoit, 2000; Nedellec et al., 2002; Dobrokhotov et al., 2003) and related entities (Collier et al., 2000; Humphreys et al., 2000; Yu et al., 2002; Yamamoto et al., 2003; Albert et al., 2003) or using terms in biomedical vocabularies (Nazarenko et al., 2001; Ruch et al., 2004; Srinivasan and Hristovski, 2004). The use of bibliographical and argumentative information (McKnight and Srinivasan 2003) has been less well studied by researchers interested in applying natural language processing to biomedical texts.</Paragraph>
    <Section position="1" start_page="8" end_page="8" type="sub_section">
      <SectionTitle>
2.1 Citations
</SectionTitle>
      <Paragraph position="0"> Originating from bibliometrics, citation analysis (White, 2003) has been used to visualize a field via a representative slice of its literature. Co-citation techniques make it possible to cluster documents by scientific paradigm or hypothesis (Noyons et al., 1999). Braam et al., (1991) have investigated co-citation as a tool to map subject-matter specialties. They found that the combination of keyword analysis and co-citation analysis was useful in revealing the cognitive content of publications. Peters et al., (1995) further explored the citation relationships and the cognitive resemblance in scientific articles. Word profile similarities of publications that were bibliographically coupled by a single, highly cited article were compared with publications that were not bibliographically coupled to that specific article. A statistically significant relationship has been established between the content of articles and their shared citations. This result will serve as basis to establish our benchmark without relevance judgments (Wu and Crestani, 2003; Soborrof et al., 2001).</Paragraph>
    </Section>
    <Section position="2" start_page="8" end_page="8" type="sub_section">
      <SectionTitle>
2.2 Argumentation in biomedical abstracts
</SectionTitle>
      <Paragraph position="0"> Scientific research is often described as a problem solving activity. In full text scientific articles this problem-solution structure has been crystallized in a fixed presentation known as Introduction, Methods, Results and Conclusion. This structure is often presented in a much-compacted version in the abstract and it has been clearly demonstrated by Schuemie et al., (2004) that abstracts contain a higher information density than full text.</Paragraph>
      <Paragraph position="1"> Correspondingly, the 4-move problem-solving structure (standardized according to ISO/ANSI guidelines) has been found quite stable in scientific reports (Orasan, 2001). Although the argumentative structure of an article is not always explicitly labeled, or can be labeled using slightly different markers (as seen in Figure 1), a similar implicit structure is common in most biomedical abstracts (Swales, 1990). Therefore, to find the most relevant argumentative status that describes the content of the article, we employed a classification method to separate the content dense sentences of the abstracts into the argumentative moves.</Paragraph>
      <Paragraph position="2"> INTRODUCTION: Chromophobe renal cell carcinoma (CCRC) comprises 5% of neoplasms of renal tubular epithelium. CCRC may have a slightly better prognosis than clear cell carcinoma, but outcome data are limited. PURPOSE: In this study, we analyzed 250 renal cell carcinomas to a) determine frequency of CCRC at our Hospital and b) analyze clinical and pathologic features of CCRCs.</Paragraph>
      <Paragraph position="3"> METHODS: A total of 250 renal carcinomas were analyzed between March 1990 and March 1999.</Paragraph>
      <Paragraph position="4"> Tumors were classified according to well-established histologic criteria to determine stage of disease; the system proposed by Robson was used. RESULTS: Of 250 renal cell carcinomas analyzed, 36 were classified as chromophobe renal cell carcinoma, representing 14% of the group studied. The tumors had an average diameter of 14 cm. Robson staging was possible in all cases, and 10 patients were stage 1) 11 stage II; 10 stage III, and five stage IV. The average follow-up period was 4 years and 18 (53%) patients were alive without disease. CONCLUSION: The highly favorable pathologic stage (RI-RII, 58%) and the fact that the majority of patients were alive and disease-free suggested a more favorable prognosis for this type of renal cell carcinoma.</Paragraph>
      <Paragraph position="5">  MEDLINE. The 4-class argumentation model is sometimes split into classes that may carry slightly different names, as illustrated in this example by the</Paragraph>
    </Section>
  </Section>
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