Merging Results by Using Predicted Retrieval Effectiveness

In this paper we proposed several merging strategies to merge the result lists of each intermediate runs in distributed MLIR. The prediction of retrieval effectiveness was used to adjust the similarity scores of documents in the result lists. We introduced three factors affecting the retrieval effectiveness, i.e., the degree of translation ambiguity, the number of unknown words and the number of relevant documents in a collection for a given query. The results show that the normalizedby-top-k merging with translation penalty and collection weight outperforms the other merging strategies except the raw-score merging.