In this article, we present a novel principled approach to passage-based (document) retrieval using fuzzy set theory. The approach formulates passage score combination according to general relevance decision principles. By operationalizing these principles using aggregation operators of fuzzy set theory, our approach justifies the common heuristics of taking the maximum constituent passage score as the overall document score. Experiments show that this heuristics is only the near best, with some fuzzy set aggregation operators stipulated in our approach being better methods. The significance of our principled approach is the applicability of many passage score combination methods, potentially bringing further performance enhancement. Experiments on several text retrieval conference collections demonstrate that our approach performs significantly better than document-based retrieval. While recent works in the literature mostly employ document-based rather than passage-based retrieval due to the common conception that document length normalization solves the problem of varying document lengths, our results show that document length normalization alone is not sufficient, especially in pseudo-relevance feedback retrieval.
|Journal||IEEE Transactions on Fuzzy Systems|
|Publication status||Published - Jul 2021|