The document discusses using a genetic algorithm and machine learning classifier to select good expansion terms for improving query results. A genetic algorithm is trained using average precision as the fitness function to select expansion term combinations. A term classifier is then trained on the selected terms to classify new candidate terms without needing user relevance judgments. The classifier approach improved results by 18.9% compared to the baseline, demonstrating the potential of this method for automatic query expansion.

3. User queries are usually too short and ambiguous

4. Several studies have concluded that the average number of terms is between 2 and 3 .

9. Inappropriate order

14. Performance drops for some queries

15. Expansion terms must be carefully chosen

17. The system extracts expansion terms from the set of relevant documents.

19. The system extracts expansion terms from the top k documents.

21. Extract terminology from top k

22. Select the expansion terms

23. Reweight expansion terms

24. Expanded query: original + expension terms

29. Measure of the query quality: Average Precision

30. Requires the user relevance judgements

31. Only known for some document collections

32. A different approach!

34. Average Precision

35. Train a term classifier with the combination of terms selected by the GA

36. The classifier can select expansion terms from docs without the user relevance judgements

41. judgements

45. features

47. Each position a candidate query term

48. 1 the term is in the query

49. Initial population: randomly generated

50. Initial query terms are always included

52. One-point crossover

53. Mutation: one bit flips

54. Elitism

56. Average Precision for a set of relevant documents Rel = [ d 1, ... , d n ] is the mean precision of all these documents:

59. Inverse document frequency of t

61. 150 first available queries

62. User relevance judgements are provided

66. GA shown more than a half of the candidate terms for expansion has no effect or worsen the query results

67. The classifier approach has achieved more than 18% of improvement..