Current Information Retrieval Systems Research Articles

Extraction of Salient Textual Patterns: Synergy Between Lexical Cohesion and Contextual Coherence

Most current information retrieval systems rely solely on lexical item repetition, which is notorious for its vulnerability. In this research, we propose a novel method for the extraction of salient textual patterns. One of our major objectives is to move away from keywords and their associated limitations in textual information retrieval. How individual sentences in text fit together to be perceived as a salient pattern is identified. A text network that exhibits textual continuity, arising from a connectionist model, is described. The network facilitates a dynamic extraction of salient textual segments by capturing semantics from two different categories of natural language, namely lexical cohesion and contextual coherence. We also present the results of an empirical study designed to compare our model with the performance of human judges in the identification of salient textual patterns. The preliminary results show that our model has the potential for automatic salient patterns discovery in text.

Information retrieval by metabrowsing

AbstractThis article investigates a new, effective browsing approach called metabrowsing. It is an alternative for current information retrieval systems, which still face six prominent difficulties. We identify and classify the difficulties and show that the metabrowsing approach alleviates the difficulties associated with query formulation and missing domain knowledge. Metabrowsing is a high‐level way of browsing through information: instead of browsing through document contents or document surrogates, the user browses through a graphical representation of the documents and their relations to the domain. The approach requires other cognitive skills from the user than what is currently required. Yet, a user evaluation in which the metabrowsing system was compared with an ordinary query‐oriented system showed only some small indicatory differences in effectiveness, efficiency, and user satisfaction. We expect that more experience with metabrowsing will result in a significantly better performance difference. Hence, our conclusion is that the development of new cognitive skills requires some time before the technologies are ready to be used.

Design and implementation of a string database query language

Current data management and information retrieval systems lack advanced string processing capabilities needed in string-oriented application areas like computational molecular biology. Several theoretical models for string processing have been proposed but they either have not been implemented in practice or the implementations are too restricted or platform-dependent to be generally useful. In this article, we introduce the language Alignment Declarations designed for string querying and restructuring. The language extends the capabilities of existing database query languages by allowing the user to define database predicates that express structural properties of strings (e.g. containment of certain patterns) or relations between several strings (e.g. similarity measures). These predicates can be created and executed within the same database session and also stored for later sessions. We also describe the design and implementation of a working system.

Aboutness from a commonsense perspective

Information retrieval (IR) is driven by a process that decides whether a document is about a query. Recent attempts spawned from a logic-based information retrieval theory have formalized properties characterizing “aboutness,” but no consensus has yet been reached. The proposed properties are largely determined by the underlying framework within which aboutness is defined. In addition, some properties are only sound within the context of a given IR model, but are not sound from the perspective of the user. For example, a common form of aboutness, namely overlapping aboutness, implies precision degrading properties such as compositional monotonicity. Therefore, the motivating question for this article is: independent of any given IR model, and examined within an information-based, abstract framework, what are commonsense properties of aboutness (and its dual, nonaboutness)? We propose a set of properties characterizing aboutness and nonaboutness from a commonsense perspective. Special attention is paid to the rules prescribing conservative behavior of aboutness with respect to information composition. The interaction between aboutness and nonaboutness is modeled via normative rules. The completeness, soundness, and consistency of the aboutness proof systems are analyzed and discussed. A case study based on monotonicity shows that many current IR systems are either monotonic or nonmonotonic. An interesting class of IR models, namely those that are conservatively monotonic, is identified.

Shifts of interactive intentions and information-seeking strategies in interactive information retrieval

Research has demonstrated that people engage in multiple types of information-seeking strategies when using information retrieval (IR) systems; unfortunately, current IR systems are designed to support only one type of information-seeking strategy: specifying queries. The limitation of the existing IR systems calls for the need to investigate how to support users as they shift from one information-seeking strategy to another in their attempts to achieve their information-seeking goals. The focus of this study is on the in-depth investigation of shifts in the micro-level of user goals—“interactive intention” and information-seeking strategies that users engage in within an information-seeking episode. Forty cases of library uses were selected from four different types of libraries for this study. The qualitative and quantitative analysis of the data identifies four types of shifts of interactive intentions and three types of information-seeking strategies. The results of the study are discussed to understand the nature of the interactive IR process, and to further suggest their implications for the design of adaptive IR systems.

Enhanced Text Retrieval Using Natural Language Processing

Etat actuel de cette methode. Quelques ameliorations basees sur l'emploi du traitement du langage naturel ont atteint finalement les moteurs de recherche du commmerce, mais il n'y a pas d'usage normalise en terminologie pour decrire leur processus

A classification approach to Boolean query reformulation

One of the difficulties in using current Boolean-based information retrieval systems is that it is hard for a user, especially a novice, to formulate an effective Boolean query. Query reformulation can be even more difficult and complex than formulation since users often have difficulty incorporating the new information gained from the previous search into the next query. In this article, query reformulation is viewed as a classification problem, that is, classifying documents as either relevant or nonrelevant. A new reformulation algorithm is proposed which builds a tree-structured classifier, called a query tree, at each reformulation from a set of feedback documents retrieved from the previous search. The query tree can easily be transformed into a Boolean query. The query tree is compared to two query reformulation algorithms on benchmark test sets (CACM, CISI, and Medlars). In most experiments, the query tree showed significant improvements in precision over the two algorithms compared in this study. We attribute this improved performance to the ability of the query tree algorithm to select good search terms and to represent the relationships among search terms into a tree structure.

Information retrieval for patient care

Doctors need clinical information during most consultations with patients, and much of this need could be satisfied by material from online sources. Advances in data communication technologies mean that multimedia...

Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. © 1995 John Wiley & Sons, Inc.

Boolean Query Reformulation with the Query Tree Classifier

One of the difficulties in using the current Boolean-based information retrieval systems is that it is hard for a user, especially a novice, to formulate an effective Boolean query. Query reformulation can be even more difficult and complex than formulation since the user can have difficulty in incorporating the new information gained from the previous search into his/her next query. In this research, query reformulation is viewed as a classification problem (i.e., classifying documents as either relevant or nonrelevant), and a new reformulation algorithm is proposed which builds a treestructured classifier (named the query tree) at each reformulation from a set of feedback documents retrieved from the previous search. The query tree can be easily transformed into a Boolean query. The query tree and two of the most important current query reformulation algorithms were compared on benchmark test sets (CACM. CISI, and MedJars). The query tree showed significant improvements over the current algorithms in most experiments. We attribute this improved performance to the ability of the query tree algorithm to select good search terms and to represent the relationships among search terms into a tree structure.

Probabilistic methods for ranking output documents in conventional Boolean retrieval systems

Current operational information retrieval systems based on Boolean searching could be radically improved through the incorporation of a weighting mechanism for ranking output documents. However, a number of previous attempts to refine conventional Boolean retrieval systems along these lines have not been fully successful because of their inherent inconsistencies and ambiguities. A detailed account of the research aimed at obtaining a more rigorous methodology is given in this article. The presented approach has been developed by extending well-known probabilistic output ranking methods that are applicable in retrieval systems in which document representations as well as search request formulations are simply sets of index terms. A series of experiments, carried out in recent years to verify some of these methods, have particularly demonstrated the value of a systematic statistical use of relevance feedback information. It is therefore expected that the application of the extended probabilistic document ranking methodology in conventional Boolean systems will also prove to be useful, and that considerable improvements in retrieval performance of these systems will be obtained. A theoretical framework used to derive the proposed output ranking scheme is described in detail. A simple illustrative example is included, followed by a thorough discussion of the suggested approach.

Grammatically-based automatic word class formation

Most previous attempts at producing word classes (thesauri) by statistical analysis have used very limited distributional information such as word co-occurrence in a document or a sentence. This paper describes an automatic procedure which uses the syntactic relations as the basis for grouping words into classes. It forms classes by grouping together nouns that occur as subject (or object) of the same verbs, and similarly by grouping together verbs occurring with the same subject or object. The program was applied to a small corpus of sentences in a subfield of pharmacology. This procedure yielded the word classes for the subfield, in good agreement with the word classes recognized by pharmacologists. The word classes can be used to describe the informational patterns that occur in texts of the subfield, to disambiguate parses of a sentence, and perhaps to improve the performance of current information retrieval systems.