Such scores are used to select and weight expansion terms within Rocchio's framework for query reweigthing. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that the former achieves better retrieval effectiveness on several performance measures. We also discuss the effect on retrieval effectiveness of the main parameters involved in automatic query expansion, such as data sparseness, query difficulty, number of selected documents, and number of selected terms, pointing out interesting relationships. The function can also be used to rank such terms. Experiments have been conducted by the authors using recent TREC data to test this function and to compare it to other scoring functions.
Retrieval performance seems to be modestly affected by parameter changes.
It first increases with query difficulty, then decreases as query difficulty is increased further. Similarly, performance increases at first when the number of relevant documents retrieved increases, then decreases gradually as the number of documents increases further. Clearly, retrieval is still a complex problem. The paper is a good read, with a solid, albeit brief, discussion of the relevant literature. The Digital Library is published by the Association for Computing Machinery.