Integrating textual and visual information for cross-language image retrieval: A trans-media dictionary approach

This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. First, we mine the relationships between text and images and employ the mined relationships to construct visual queries from textual ones. Then, the retrieval results of textual and visual queries are combined. To evaluate the proposed approach, we conduct English monolingual and Chinese–English cross-language retrieval experiments. The selection of suitable textual query terms to construct visual queries is the major issue. Experimental results show that the proposed approach improves retrieval performance, and use of nouns is appropriate to generate visual queries.     

Task complexity, problem structure and information actions: Integrating studies on information seeking and retrieval

Analyzing actions to be supported by information and information retrieval (IR) systems is vital for understanding the needs of different types of information, search strategies and relevance assessments, in short, understanding IR. A necessary condition for this understanding is to link results from information seeking studies to the body of knowledge by IR studies. The actions to be focused on in this paper are tasks from the angle of problem solving. I will analyze certain features of work tasks and relate these features to types of information people are looking for and using in their tasks, patterning of search strategies for obtaining information and relevance assessments in choosing retrieved documents. The major claim is that these information activities are systematically connected to task complexity and structure of the problem at hand. The argumentation is based on both theoretical and empirical results from studies on information retrieval and seeking.     

Teaching on-line retrieval systems at the University of California, Los Angeles

All library school students should be provided with an opportunity to obtain hands-on experience in using such bibliographic retrieval systems as ORBIT, DIALOG, OCLC, etc. Yet, such training is both costly and time consumming. Two key issues that must be resolved in order to make the training more efficient and more effective are: (1) the integration of training in the curriculum as a module in cataloging and reference courses or as a separate course; and (2) the method of training which may include the use of videotapes, demonstrations, training manuals, etc. The teaching program at UCLA provides for discussion and demonstration of on-line retrieval techniques in the basic courses and advanced search training in a separate course using a specially prepared training manual.     

Bibliometric cartography of information retrieval research by using co-word analysis

The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987–1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987–1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987–1991 and 1992–1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes.     

Visualizing and mapping the intellectual structure of information retrieval

Information retrieval is a long established subfield of library and information science. Since its inception in the early- to mid -1950s, it has grown as a result, in part, of well-regarded retrieval system evaluation exercises/campaigns, the proliferation of Web search engines, and the expansion of digital libraries. Although researchers have examined the intellectual structure and nature of the general field of library and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visualizations of citation behaviors. Citation data for 10 years (2000-2009) were retrieved from the Web of Science and analyzed using existing visualization techniques. Our results address information retrieval’s co-authorship network, highly productive authors, highly cited journals and papers, author-assigned keywords, active institutions, and the import of ideas from other disciplines.     

Looking back: On relevance,probabilistic indexing and information retrieval

Forty-eight years ago Maron and Kuhns published their paper, “On Relevance, Probabilistic Indexing and Information Retrieval” (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the most widely cited papers in the field of information retrieval, many researchers today may not be familiar with its influence. This paper describes the Maron and Kuhns article and the influence that it has had on the field of information retrieval.     

Intelligent information retrieval: Part IV. Testing the timing of two information retrieval devices in a naturalistic setting

This article is Part IV in a series of articles that report the results of a two year research program the purpose of which is to design and test intelligent information retrieval (IR) devices for undergraduates researching a social science term paper. The devices are task-facilitating, helping the undergraduate perform the task of researching and writing a term paper, and they provide a mold which takes the students’ amorphous conception of their information need, turning it into an effective query to the IR system. The present article reports the results of two studies which tested an uncertainty expansion IR device and an uncertainty reduction IR device in naturalistic settings. The devices are designed to be given at different stages of Kuhlthau’s information search process (ISP). In both studies, undergraduates were randomly assigned to either the test group, who received the device intervention, or a comparison group. In Study 1, we found that the comparison group received a higher mean mark than students in the uncertainty expansion group. In Study 2, we found that the uncertainty reduction group received a higher mean mark than the comparison group when the device was given later in the student’s ISP. We conclude that the timing of the device interventions is crucial to their potential efficacy.     

Structured queries,language modeling,and relevance modeling in cross-language information retrieval

Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries––one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage.     

Intention in information systems adoption and use: Current state and research directions

Intention has been a key dependent variable in information system (IS) research for the last several decades. It features in various IS acceptance models including the technology acceptance model (TAM), the elaboration likelihood model, the IS success model, and the unified theory of acceptance and use of technology (UTAUT). In the context of IS adoption and use, intention has been portrayed in different forms including intention to adopt, intention to use, intention to continue use, intention to discontinue, and intention to switch. It has been modeled as an antecedent to behavior, a consequent of behavior, and a proxy for behavior. Prior studies of intention have been contextualized in a various settings involving use contexts (i.e., voluntary vs. mandatory use), populations (i.e., non-adopters, adopters, users), respondents (i.e., students, employees), and duration of use (i.e., limited vs. indefinite time), have used cross-sectional and longitudinal empirical designs, and employed different measurement instruments. This editorial provides a brief review of extant IS literature on intention, highlights underlying issues, and proposes directions for future research.     

Principles, procedures and rules in an expert system for information retrieval

An expert system was developed in the area of information retrieval, with the objective of performing the job of an information specialist, who assists users in selecting the right vocabulary terms for a database search.The system is composed of two components: One is the knowledge base, represented as a semantic network, in which the nodes are words, concepts, phrases, comprising a vocabulary of the application area and the links express semantic relationships between those nodes. The second component is the rules, or procedures, which operate upon the knowledge-base, analogous to the decision rules or work patterns of the information specialist.Two major stages comprise the consulting process of the system: During the “search” stage relevant knowledge in the semantic network is activated, and search and evaluation rules are applied in order to find appropriate vocabulary terms to represent the user's problem. During the “suggest” stage those terms are further evaluated, dynamically rank-ordered according to relevancy, and suggested to the user. Explanations to the findings can be provided by the system and backtracking is possible in order to find alternatives in case some suggested term is rejected by the user.This article presents the principle, procedures and rules which are utilized in the expert system.     

Problems of music information retrieval in the real world

Although a substantial number of research projects have addressed music information retrieval over the past three decades, the field is still very immature. Few of these projects involve complex (polyphonic) music; methods for evaluation are at a very primitive stage of development; none of the projects tackles the problem of realistically large-scale databases. Many problems to be faced are due to the nature of music itself. Among these are issues in human perception and cognition of music, especially as they concern the recognizability of a musical phrase. This paper considers some of the most fundamental problems in music information retrieval, challenging the common assumption that searching on pitch (or pitch-contour) alone is likely to be satisfactory for all purposes. This assumption may indeed be true for most monophonic (single-voice) music, but it is certainly inadequate for polyphonic (multi-voice) music. Even in the monophonic case it can lead to misleading results. The fact, long recognized in projects involving monophonic music, that a recognizable passage is usually not identical with the search pattern means that approximate matching is almost always necessary, yet this too is severely complicated by the demands of polyphonic music. Almost all text-IR methods rely on identifying approximate units of meaning, that is, words. A fundamental problem in music IR is that locating such units is extremely difficult, perhaps impossible.     

高校图书馆外文资料利用现状与对策探讨

外文资料对促进高校的教学和科研起着非常重要的作用.针对高校图书馆外文资料利用率一直偏低的现状,文章结合从事外文书刊工作的经验,提出了开发和利用外文资料的相应对策.     

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.     

Automatic,semantics-based indexing of natural language texts for information retrieval systems

The fundamental idea of the work reported here is to extract index phrases from texts with the help of a single word concept dictionary and a thesaurus containing relations among concepts. The work is based on the fact, that, within every phrase, the single words the phrase is composed of are related in a certain well denned manner, the type of relations holding between concepts depending only on the concepts themselves. Therefore relations can be stored in a semantic network. The algorithm described extracts single word concepts from texts and combines them to phrases using the semantic relations between these concepts, which are stored in the network. The results obtained show that phrase extraction from texts by this semantic method is possible and offers many advantages over other (purely syntactic or statistic) methods concerning preciseness and completeness of the meaning representation of the text. But the results show, too, that some syntactic and morphologic “filtering” should be included for effectivity reasons.