We studied the effectiveness of a new class of contextdependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF ), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the contextdependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the contextindependent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.

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While term independence is a widely held assumption in most of the established information retrieval approaches, it is clearly not true and various works in the past have investigated a relaxation of the assumption. One approach is to use n-grams in document representation instead of unigrams. However, the majority of early works on n-grams obtained only modest performance improvement. On the other hand, the use of information based on supporting terms or "contexts" of queries has been found to be promising. In particular, recent studies showed that using new context-dependent term weights improved the performance of relevance feedback (RF) retrieval compared with using traditional bag-of-words BM25 term weights. Calculation of the new term weights requires an estimation of the local probability of relevance of each query term occurrence. In previous studies, the estimation of this probability was based on unigrams that occur in the neighborhood of a query term. We explore an integration of the n-gram and context approaches by computing context-dependent term weights based on a mixture of unigrams and bigrams. Extensive experiments are performed using the title queries of the Text Retrieval Conference (TREC)-6, TREC-7, TREC-8, and TREC-2005 collections, for RF with relevance judgment of either the top 10 or top 20 documents of an initial retrieval. We identify some crucial elements needed in the use of bigrams in our methods, such as proper inverse document frequency (IDF) weighting of the bigrams and noise reduction by pruning bigrams with large document frequency values. We show that enhancing context-dependent term weights with bigrams is effective in further improving retrieval performance.

A new principles framework is presented for retrieval evaluation of ranked outputs. It applies decision theory to model relevance decision preferences and shows that the Probability Ranking Principle (PRP) specifies optimal ranking. It has two new components, namely a probabilistic evaluation model and a general measure of retrieval effectiveness. Its probabilities may be interpreted as subjective or objective ones. Its performance measure is the expected weighted rank which is the weighted average rank of a retrieval list. Starting from this measure, the expected forward rank and some existing retrieval effectiveness measures (e.g., top n precision and discounted cumulative gain) are instantiated using suitable weighting schemes after making certain assumptions. The significance of these instantiations is that the ranking prescribed by PRP is shown to be optimal simultaneously for all these existing performance measures. In addition, the optimal expected weighted rank may be used to normalize the expected weighted rank of retrieval systems for (summary) performance comparison (across different topics) between systems. The framework also extends PRP and our evaluation model to handle graded relevance, thereby generalizing the discussed, existing measures (e.g., top n precision) and probabilistic retrieval models for graded relevance.

The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for retrieval schemes involving query expansion, as in pseudo-relevance feedback (PRF), the retrieval time based on an inverted index increases linearly with the number of expansion terms. In this regard, we have examined the use of a forward index, which consists of the mapping of each document to its constituent terms. We propose a novel forward indexbased reranking scheme to shorten the PRF retrieval time. In our method, a first retrieval of the original query is performed using an inverted index, and then a forward index is employed for the PRF part. We have studied several new forward indexes, including using a novel spstring data structure and the weighted variable bit-block compression (wvbc) signature. With modern hardware such as solid-state drives (SSDs) and sufficiently large main memory, forward index methods are particularly promising. We find that with the whole index stored in main memory, PRF retrieval using a spstring or wvbc forward index excels in time efficiency over an inverted index, being able to obtain the same levels of performance measures at shorter times. . 2015. Fast-forward index methods for pseudo-relevance feedback retrieval.