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An evaluation of evolved term-weighting schemes in information retrieval

机译:信息检索中演化术语加权方案的评估

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This paper presents an evaluation of evolved term-weighting schemes on short, medium and long TREC queries. A previously evolved global (collection-wide) term-weighting scheme is evaluated on unseen TREC data and is shown to increase mean average precision over idf. A local (within-document) evolved term-weighting scheme is presented which is dependent on the best performing global scheme. The full evolved scheme (i.e. the combined local and global scheme) is compared to both the BM25 scheme and the Pivoted Normalisation scheme.Our results show that the local evolved solution does not perform well on some collections due to its document normalisation properties and we conclude that Okapi-tf can be tuned to interact effectively with the evolved global weighting scheme presented and increase mean average precision over the standard BM25 scheme.
机译:本文介绍了对短,中和长TREC查询的演化术语加权方案的评估。对以前看不见的TREC数据评估了以前发展的全局(全馆藏)术语加权方案,结果表明它比 idf 提高了平均平均精度。提出了一种本地(文档内)演进的术语加权方案,该方案取决于性能最佳的全局方案。将完整的演化方案(即组合的局部和全局方案)与BM25方案和Pivoted Normalization方案进行了比较。我们的结果表明,局部演化的解决方案由于其文档归一化性质而在某些集合上表现不佳,因此得出结论可以对 Okapi-tf 进行调整,使其与提出的演进的全局加权方案进行有效交互,并提高标准BM25方案的平均平均精度。

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