Name
Abstract | ||
|---|---|---|
The task of TREC 2006 Genomics Track is to retrieve passages (from part to paragraph) from full-text HTML biomedical journal papers to answer the structured ques- tions from real biologists. A system for such task needs to be able to parse the HTML free-texts (convert the HTML free-texts into plain texts) and pinpoint the most relevant passage(s) within documents for the specified question. This task is accomplished in three steps in our system. The first step is to parse the HTML articles and partition them into paragraphs. The second step is to retrieve the relevant paragraphs. The third step is to identify the most relevant passages within paragraphs and finally rank those passages. We are interested in 1. How does a con- cept-based IR model perform on structured queries com- paring to Okapi? 2. Will the query expansion based on domain knowledge increase retrieval effectiveness? 3. Will our abbreviation database from MEDLINE help im- prove query expansion and will the abbreviation disam- biguation help improve the ranking? The experiment re- sults show that our concept-based IR model works better than the Okapi; query expansion based on domain knowl- edge is important, especially for those queries with very few relevant documents; an abbreviation database for query expansion and disambiguation is helpful for passage retrieval. |
Year | Venue | Keywords |
|---|---|---|
2006 | TREC | domain knowledge,query expansion |
Field | DocType | Citations |
Data mining,Query expansion,Information retrieval,Computer science,Genomics,Natural language processing,Artificial intelligence | Conference | 12 |
PageRank | References | Authors |
0.71 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhou | 1 | 112 | 27.01 |
| Clement T. Yu | 2 | 3171 | 1419.96 |
| Vetle I. Torvik | 3 | 430 | 27.15 |
| Neil R. Smalheiser | 4 | 658 | 57.50 |