SNUMedinfo at CLEFeHealth2013 Task 3

Abstract

This paper describes the participation of the SNUMedinfo team at the CLEFeHealth2013 task 3. We submitted 7 runs in total: 1 baseline run using query likelihood model in Indri search engine; 3 runs using passage based language model; 3 runs using passage based language model with lexical query expansion. We tried to incorporate passage-based score into ranking model to reflect the degree of query term cohesion per each document.

Keywords

• Passage based language model
• Query expansion
• Web document
• Medical information retrieval
• Indri

1. Introduction

2.1 Baseline run

2.2 Passage based language model

We submitted 3 runs using passage based language model [4]. We combined maxscoring passage-based relevance score with unigram language model score. Many web pages contain hierarchical category menu or tables, which does not necessarily represent core topic information. We tried to incorporate passage-based score into ranking model to reflect the degree of query term cohesion. Different weighting parameter is applied on each run. In all 3 runs, only title field is used as query. Experimental results are described in Table 2.

2.3 Passage based language model with lexical query expansion

In addition to section 2.2, we applied lexical query expansion method. UMLS concepts in queries are recognized using MetaMap [5], and then original query is expanded with UMLS preferred terms. Only terms occurring in the discharge summary is chosen for expansion. For MEDINFO.2.3.noadd, only title field is used as query. For MEDINFO.3.3.noadd, title and desc field is used as query. For MEDINFO.4.3.noadd, title, desc and narr field is used as query. Experimental results are described in Table 3.

3. Conclusion

We submitted 6 runs all based on passage based retrieval model. Baseline retrieval model is shown to be quite effective. However, contrary to our intention, passage based retrieval score did more harm than good compared to our baseline. We hope to explore more effective method in the future study.

4. Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP)(2010-0028631). The Shared Annotated Resources (ShARe) project is funded by the United States National Institutes of Health with grant number R01GM090187.

5. References

1. 2. 3. 4. 5.

References

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