Literature storage and retrieval system using a table-top computer

A dialogue-program is described which handles crossreferences within a given set of keywords, author names, journal names and specified reference numbers. It uses the set of basic statements available in a WANG 2200T version which is connected to a floppy-diskette. The program has rather convenient features for editing and processing of literature data.     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.     

The economic evaluation of information storage and retrieval systems

This paper describes a formal standardized procedure for the decision-making process for the purchase or rejection of an information storage and retrieval system. The interaction of both the purchaser of the system and its potential users with the various models of the system (such as cost-time-volume models and performance evaluation models) ensures that the purchase decision for a given system is affected by all possible constraints and is universally acceptable. If either purchaser or users find a particular system unacceptable, the procedure either rejects it or institutes modifications (within given constraints) until a generally acceptable system is determined, if one exists.     

Integration of user profiles: models and experiments in information retrieval

One difficult problem in information retrieval (IR) is the proper interpretation of user queries. It is extremely hard for users to express their information needs in a specific yet exhaustive way. In an effort to alleviate this problem, two theoretical models have been proposed to utilize user characteristics maintained in the form of a user profile. Although the idea of integrating user profiles into an IR system is intuitively appealing, and the models seem viable, no research to date has established a foundation for the roles of user profiles in such a system. Aiming at the investigation of the roles of user profiles, therefore, this study first identifies and extends various query/profile interaction models to provide a ground upon which the investigation can be undertaken. From a continuum of models characterized on the basis of interaction types, metrics, and parameters, nearly 400 models are chosen to investigate the “model space.” New measures are developed based on the notion of user satisfaction/frustration. In addition, three different criteria are used to guide users in making judgments on the quality of retrieved items. Analysis of the data obtained from the experiments shows that, for a wide variety of criteria and metrics, there are always some query/profile interaction models that outperform the query alone model. In addition, preferable characteristics for different criteria are identified in terms of interaction types, parameters, and metrics.     

The Dilution/Concentration conditions for cross-language information retrieval models

Experimental results of cross-language information retrieval (CLIR) do not indicate why a model fails or how a model could be improved. One basic research question is thus whether it is possible to provide conditions by which one can evaluate any existing or new CLIR strategy analytically and one can improve the design of CLIR models. Inspired by the heuristics in monolingual IR, we introduce in this paper Dilution/Concentration (D/C) conditions to characterize good CLIR models based on direct intuitions under artificial settings. The conditions, derived from first principles in CLIR, generalize the idea of query structuring approach. Empirical results with state-of-the-art CLIR models show that when a condition is not satisfied, it often indicates non-optimality of the method. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies the conditions. Lastly, we propose, by following the D/C conditions, several novel CLIR models based on the information-based models, which again shows that the D/C conditions are efficient to feature good CLIR models.     

Adapting information retrieval to query contexts

In current IR approaches documents are retrieved only according to the terms specified in the query. The same answers are returned for the same query whatever the user and the search goal are. In reality, many other contextual factors strongly influence document’s relevance and they should be taken into account in IR operations. This paper proposes a method, based on language modeling, to integrate several contextual factors so that document ranking will be adapted to the specific query contexts. We will consider three contextual factors in this paper: the topic domain of the query, the characteristics of the document collection, as well as context words within the query. Each contextual factor is used to generate a new query language model to specify some aspect of the information need. All these query models are then combined together to produce a more complete model for the underlying information need. Our experiments on TREC collections show that each contextual factor can positively influence the IR effectiveness and the combined model results in the highest effectiveness. This study shows that it is both beneficial and feasible to integrate more contextual factors in the current IR practice.     

Using information gain to improve multi-modal information retrieval systems

Nowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have become an active research direction. In the past few years, a lot of content-based image retrieval (CBIR) systems have been developed. However, despite the progress achieved in the CBIR, the retrieval accuracy of current systems is still limited and often worse than only textual information retrieval systems. In this paper, we propose to combine content-based and text-based approaches to multi-modal retrieval in order to achieve better results and overcome the lacks of these techniques when they are taken separately. For this purpose, we use a medical collection that includes both images and non-structured text. We retrieve images from a CBIR system and textual information through a traditional information retrieval system. Then, we combine the results obtained from both systems in order to improve the final performance. Furthermore, we use the information gain (IG) measure to reduce and improve the textual information included in multi-modal information retrieval systems. We have carried out several experiments that combine this reduction technique with a visual and textual information merger. The results obtained are highly promising and show the profit obtained when textual information is managed to improve conventional multi-modal systems.     

Effective contact recommendation in social networks by adaptation of information retrieval models

We investigate a novel perspective to the development of effective algorithms for contact recommendation in social networks, where the problem consists of automatically predicting people that a given user may wish or benefit from connecting to in the network. Specifically, we explore the connection between contact recommendation and the text information retrieval (IR) task, by investigating the adaptation of IR models (classical and supervised) for recommending people in social networks, using only the structure of these networks.We first explore the use of adapted unsupervised IR models as direct standalone recommender systems. Seeking additional effectiveness enhancements, we further explore the use of IR models as neighbor selection methods, in place of common similarity measures, in user-based and item-based nearest-neighbors (kNN) collaborative filtering approaches. On top of this, we investigate the application of learning to rank approaches borrowed from text IR to achieve additional improvements.We report thorough experiments over data obtained from Twitter and Facebook where we observe that IR models, particularly BM25, are competitive compared to state-of-the art contact recommendation methods. We provide further empirical analysis of the additional effectiveness that can be achieved by the integration of IR models into kNN and learning to rank schemes. Our research shows that the IR models are effective in three roles: as direct contact recommenders, as neighbor selectors in collaborative filtering and as samplers and features in learning to rank.     

Comparison of cairs and status information storage and retrieval software over a 3 month period

Following a period of about a year in which “desk” assessments were made of information storage and retrieval packages to narrow the options down, the software packages CAIRS and STATUS were evaluated operationally in the Ministry of Agriculture, Fisheries and Food, during a the period between February and July 1981. The packages, loaded on to separate computers, were used to build up databases and databanks based on existing technical information/library services. As far as possible, direct comparison were made from implementation through to retrievals. A range of types of services from bibliographic (library) through to factual text and numeric data ranging across several Divisions of the Ministry were covered. Input and retrieval was remotely via GPO lines and private wire networks. The paper presents the major conclusions with particular emphasis on the databank component of the trial.     

计算机网络在图书情报现代化中的应用

计算机网络是计算机技术与通信技术相结合的产物,是许多数据设备互相连接进行数据通讯的信息系统.近年来由于计算机技术和通信技术的飞速发展使计算机网络技术广泛而成熟地应用于各行各业,同样,计算机网络在图书情报自动化中也得到了广泛的应用;其应用模式也由原来的单机型向网络化、集成化方向发展,其应用范围也由局域扩大到大城市以至更大的区域.     

Adapting information retrieval systems to user queries

Users enter queries that are short as well as long. The aim of this work is to evaluate techniques that can enable information retrieval (IR) systems to automatically adapt to perform better on such queries. By adaptation we refer to (1) modifications to the queries via user interaction, and (2) detecting that the original query is not a good candidate for modification. We show that the former has the potential to improve mean average precision (MAP) of long and short queries by 40% and 30% respectively, and that simple user interaction can help towards this goal. We observed that after inspecting the options presented to them, users frequently did not select any. We present techniques in this paper to determine beforehand the utility of user interaction to avoid this waste of time and effort. We show that our techniques can provide IR systems with the ability to detect and avoid interaction for unpromising queries without a significant drop in overall performance.     

A decision-theoretic approach to the evaluation of information retrieval systems

Evaluation research on information retrieval (IR) systems has thus far been narrowly focused and disjointed. This research attempts to narrow the gap by providing a comprehensive and integrated multiple criteria decision-theoretic approach for the evaluation of IR systems. The approach, which is based on the Analytic Hierarchy Process (AHP), is illustrated in the context of a domain-specific IR system. The novelty of this approach lies in the focus on the user aspect and the application of decision-making theories in the IR field.     

A novice user's interface to information retrieval systems

This report describes the design of an interface for information retrieval systems intended for novice users. The main design goal is to provide a system that can be used without training, external help or documentation. Design decisions are largely based on the literature on human factors in interactive computer systems. The major design features of the system are the use of menus and fill-in-the blank modes of command entry, display windowing and an extensive two-level online help facility providing step by step guidance for the user during command/query entry. A preliminary evaluation of the system indicates that it can in fact be used by people unfamiliar with information retrieval systems, without the aid of any external help, documentation, or training.