According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document's passages. However, the main source of passage-based information utilized was passage-query similarities. We address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query; the passage ranking is also induced using a learning-to-rank approach. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of passages used for learning a passage ranker. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document's passage most highly ranked.
In @cite_9 , features based on passage-query similarities were used to learn a document ranker . The induced ranking was fused with a query-similarity-based document ranking. Our method, described in Section , generalizes this approach by using many more passage features, integrating the resultant passage-based document ranking with that produced by learning to rank documents, and applying state-of-the-art learning-to-rank approaches. While is highly effective, it is outperformed by our best performing method.
@cite_9
2158738673
We present an application of Hidden Markov Models to supervised document classification and ranking. We consider a family of models that take into account the fact that relevant documents may contain irrelevant passages; the originality of the model is that it does not explicitly segment documents but rather considers all possible segmentations in its final score. This model generalizes the multinomial Naive Bayes and it is derived from a more general model for different access tasks. The model is evaluated on the REUTERS test collection and compared to the multinomial Naive Bayes model. It is shown to be more robust with respect to the training set size and to improve the performance both for ranking and classification, specially for classes with few training examples.