A Comparison of Techniques for Classification and Ad Hoc Retrieval of Biomedical Documents

participated in both the classification and ad hoc retrieval tasks of the TREC 2005 Genomics Track. To better understand the text classification techniques that lead to improved performance, we applied a set of general purpose biomedical document classification systems to the four triage tasks, varying one system feature or text processing technique at a time. We found that our best and most consistent system consisted of a voting perceptron classifier, chi-square feature selection on full text articles, binary feature weighting, stemming and stopping, and pre-filtering based on the MeSH term Mice. This system approached, but did not surpass, the performance of the best TREC entry for each of the four tasks. Full text provided a substantial benefit over only title plus abstract. Other common techniques such as inverse-document frequency feature weighting, and cosine normalization were ineffective. For the ad hoc retrieval task, we used Zettair search engine. Both of our submissions used Okapi measure with the parameters optimized using the sample topics that were provided. Two different query sets were used in our runs; one with all the words and the other with only the keywords from the topic file. Queries with only keywords consistently outperformed queries with all words from the topic file. Optimization of the Okapi parameters improved our performance.