Supporting Full-Text Information Retrieval with a Persistent Object Store
Full-text information retrieval systems have unusual and challenging data management requirements. Attempts have been made to satisfy these requirements using traditional (e.g., relational) database management systems. Those attempts, however, have produced rather discouraging results. Instead, information retrieval systems typically use custom data management facilities that require significant development effort and usually do not provide all of the services available from a standard database management system. Advanced data management systems, such as object-oriented database management systems and persistent object stores, offer a reasonable alternative to the two previous approaches. We have taken an existing information retrieval system (INQUERY) and substituted a persistent object store (Mneme) for the portion of the custom data management system that manages an inverted file index. The result is an improvement in performance and significant opportunities for the information retrieval system to take advantage of the standard data management services provided by the persistent object store. We describe our implementation, present performance results on a variety of document collections, and discuss the advantages of using a persistent object store to support information retrieval.

title: Precision prediction based on ranked list coherence
abstract: We introduce a statistical measure of the coherence of a list of documents called the clarity score. Starting with a document list ranked by the query-likelihood retrieval model, we demonstrate the score's relationship to query ambiguity with respect to the collection. We also show that the clarity score is correlated with the average precision of a query and lay the groundwork for useful predictions by discussing a method of setting decision thresholds automatically. We then show that passage-based clarity scores correlate with average-precision measures of ranked lists of passages, where a passage is judged relevant if it contains correct answer text, which extends the basic method to passage-based systems. Next, we introduce variants of document-based clarity scores to improve the robustness, applicability, and predictive ability of clarity scores. In particular, we introduce the ranked list clarity score that can be computed with only a ranked list of documents, and the weighted clarity score where query terms contribute more than other terms. Finally, we show an approach to predicting queries that perform poorly on query expansion that uses techniques expanding on the ideas presented earlier.

title: Boosting relevance model performance with query term dependence

title: Optimization strategies for complex queries
abstract: Previous research into the efficiency of text retrieval systems has dealt primarily with methods that consider inverted lists in sequence; these methods are known as term-at-a-time methods. However, the literature for optimizing document-at-a-time systems remains sparse.We present an improvement to the max_score optimization, which is the most efficient known document-at-a-time scoring method. Like max_score, our technique, called term bounded max_score, is guaranteed to return exactly the same scores and documents as an unoptimized evaluation, which is particularly useful for query model research. We simulated our technique to explore the problem space, then implemented it in Indri, our large scale language modeling search engine. Tests with the GOV2 corpus on title queries show our method to be 23% faster than max_score alone, and 61% faster than our document-at-a-time baseline. Our optimized query times are competitive with conventional term-at-a-time systems on this year's TREC Terabyte task.

title: Lexical ambiguity and information retrieval
abstract: Lexical ambiguity is a pervasive problem in natural language processing. However, little quantitative information is available about the extent of the problem or about the impact that it has on information retrieval systems. We report on an analysis of lexical ambiguity in information retrieval test collections and on experiments to determine the utility of word meanings for separating relevant from nonrelevant documents. The experiments show that there is considerable ambiguity even in a specialized database. Word senses provide a significant separation between relevant and nonrelevant documents, but several factors contribute to determining whether disambiguation will make an improvement in performance. For example, resolving lexical ambiguity was found to have little impact on retrieval effectiveness for documents that have many words in common with the query. Other uses of word sense disambiguation in an information retrieval context are discussed.

title: Formal multiple-bernoulli models for language modeling
abstract: Statistical language modeling allows formal methods to be applied to information retrieval. As a result, such methods are preferred over their heuristic tf.idf -based counterparts. In language modeling, a statistical model is estimated for each document in the corpus. Documents are then scored by the likelihood the query was generated by the document’s model. Typically, the underlying model is assumed to be of a specific parametric form. In the past, a number of different assumptions have been made about this distribution. In , documents were modeled by a multiple-Bernoulli distribution. However, the estimation and smoothing techniques used to estimate the model were non-standard and somewhat heuristic. The predominant modeling assumption used today, as described in , is to model documents by a multinomial distribution. Such models may be smoothed in a number of ways . Among these is Bayesian (Dirichlet) smoothing that takes a formal, Bayesian approach to smoothing by assuming a Dirichlet prior over the document model. Unlike Ponte and Croft’s multiple-Bernoulli estimation techniques, the multinomial assumption combined with Bayesian smoothing results in a completely formal statistical model. In this paper, we revisit the multiple-Bernoulli assumption and formalize it by taking a Bayesian approach to estimating smoothed document models.

title: Ernest Davis, Representations of Commonsense Knowledge