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Classification, indexing and abstracting can all be regarded as summarisations of the content of a document. A model of text comprehension by indexers (including classifiers and abstractors) is presented, based on task descriptions which indicate that the comprehension of text for indexing differs from normal fluent reading in respect of: operational time constraints, which lead to text being scanned rapidly for perceptual cues to aid gist comprehension; comprehension being task oriented rather than learning oriented, and being followed immediately by the production of an abstract, index, or classification; and the automaticity of processing of text by experienced indexers working within a restricted range of text types. The evidence for the interplay of perceptual and conceptual processing of text under conditions of rapid scanning is reviewed. The allocation of mental resources to text processing is discussed, and a cognitive process model of abstracting, indexing and classification is described.

A number of approaches and algorithms have been proposed over the years as a basis for automatic indexing. Many of these approaches suffer from precision inefficiency at low recall. The choice of indexing units has a great impact on search system effectiveness. The authors dive beyond simple terms indexing to propose a framework for multi-word terms (MWT) filtering and indexing.

In this paper, the authors rely on ranking MWT to filter them, keeping the most effective ones for the indexing process. The proposed model is based on filtering MWT according to their ability to capture the document topic and distinguish between different documents from the same collection. The authors rely on the hypothesis that the best MWT are those that achieve the greatest association degree. The experiments are carried out with English and French languages data sets.

The results indicate that this approach achieved precision enhancements at low recall, and it performed better than more advanced models based on terms dependencies.

Using and testing different association measures to select MWT that best describe the documents to enhance the precision in the first retrieved documents.

This paper is concerned with recent work in the theory of information retrieval. More particularly, it is concerned with theories which tackle the problem of retrieval performance, in a sense which will be explained. The aim is not an exhaustive survey of such work; rather it is an analysis and synthesis of those contributions which I feel to be important or find interesting.

As a consequence of rapid urbanisation and globalisation, cities have become the engines of population and economic growth. Hence, natural resources in and around the cities have been exposed to externalities of urban development processes. This paper introduces a new sustainability assessment approach that is tested in a pilot study. The paper aims to assist policy‐makers and planners investigating the impacts of development on environmental systems, and produce effective policies for sustainable urban development.

The paper introduces an indicator‐based indexing model entitled “Indexing Model for the Assessment of Sustainable Urban Ecosystems” (ASSURE). The ASSURE indexing model produces a set of micro‐level environmental sustainability indices that is aimed to be used in the evaluation and monitoring of the interaction between human activities and urban ecosystems. The model is an innovative approach designed to assess the resilience of ecosystems towards impacts of current development plans and the results serve as a guide for policy‐makers to take actions towards achieving sustainability.

The indexing model has been tested in a pilot case study within the Gold Coast City, Queensland, Australia. This paper presents the methodology of the model and outlines the preliminary findings of the pilot study. The paper concludes with a discussion on the findings and recommendations put forward for future development and implementation of the model.

Presently, there is a few sustainability indices developed to measure the sustainability at local, regional, national and international levels. However, due to challenges in data collection difficulties and availability of local data, there is no effective assessment model at the micro‐level that the assessment of urban ecosystem sustainability accurately. The model introduced in this paper fills this gap by focusing on parcel‐scale and benchmarking the environmental performance in micro‐level.

A probabilistic model previously used in relevance feedback is adapted for use in automatic indexing of documents (in the sense of imitating human indexers). The model fits with previous work in this area (the ‘adhesion coefficient’ method), in effect merely suggesting a different way of arriving at the adhesion coefficients. Methods for the application of the model are proposed. The independence assumptions used in the model are interpreted, and the possibility of a dependence model is discussed.

The development of a given discipline in science and technology often depends on the availability of theories capable of describing the processes which control the field and of modelling the interactions between these processes. The absence of an accepted theory of information retrieval has been blamed for the relative disorder and the lack of technical advances in the area. The main mathematical approaches to information retrieval are examined in this study, including both algebraic and probabilistic models, and the difficulties which impede the formalization of information retrieval processes are described. A number of developments are covered where new theoretical understandings have directly led to the improvement of retrieval techniques and operations.

The purpose of this paper is to examine the value of active fund management using a sample of hybrid mutual funds.

Instead of using traditional risk‐adjusted measures, the paper employs an alternative attribution return methodology where the actual monthly fund return is compared to the return that would have been earned by the indexing strategy that best reflects the fund's prior month allocation. Value is measured by defining a fund's attribution return as the difference between a fund's actual month t return and the return that would have been generated in month t by the indexing strategy that most closely approximates the fund's month t−1 portfolio allocation.

It is found that hybrid funds as a group do not add value and that this underperformance does not appear to be driven by the poor performance of non‐surviving funds. However, these funds perform significantly better than the style benchmark under weak vs strong stock market conditions. This performance difference between bull and bear market conditions suggests some hedge fund‐like downside protection that may offer a reason why investors choose these funds despite the funds’ average underperformance and despite their higher costs relative to index funds.

The contribution of this paper is twofold. First, it concentrates on hybrid mutual funds, which despite a surge in their interest over the last five years have attracted very little academic study. Second, in the implementation of its non‐traditional performance measure, it employed daily fund returns, stock market indices and bond market indices as opposed to the monthly or quarterly data used in other related studies.

The purposes of this study were to measure the relevance status of Index Islamicus, evaluate the semantic correlation between a query and documents and inquire the basis of its rank. Sorting the retrieved results from the most relevant to the least relevant is the common option of an information retrieval system. This sorting mechanism or relevance judgment is computed by measuring closeness of query with its documents.

Forming up 100 queries on Islamic History and Civilizations, with two indexing elements (keyword and concept), a laboratory experiment was generated on its first ten items of the rank. Throughout an experimental research design, the relevance status value formula was used to measure system-computed rank and compare it with mean average precision.

The results showed that the average status value of Index Islamicus’s ranking on relevance criterion was 18 per cent effective in terms of retrieving precise documents. Despite the main focus of this study being only on one subject domain and the items calculated were only 1,000, this small percentage of its ranking mechanism proved that semantic correlations between queries with subject domain did not achieve the satisfactory level.

Implication of this study could be a guideline for further research on ranking mechanism of other search engines because the limitation of this study was Index Islamicus being the only database, which was the focus of this study.

Throughout this study, Index Islamicus would be benefited knowing the status of its ranking mechanism as well as other databases can make further research on their own ranking method following this study.

Researchers and vendors of online databases can ensure their users a true platform of search engine with a proper ranking list.

Relevance status value model for Index Islamicus on Islamic History and Civilization that allows the system to rank documents according to the match between document and query and gives the idea of a better index. The model improves the system’s ranking mechanism, and promotes the use of semantic relationships. This research promotes the computation of relevance status value by domain for capturing subject-specific relevance criteria and semantic relationships.

THE training model to be discussed is based on an integrated set of manual and mechanised indexing systems, all handling the same body of information from a limited subject field. By extending the scope of the model's operations to include prior and subsequent activities like the selection and abstracting of the documents to be indexed, and the preparation and dissemination of material through the use of the indexes, the model may be used for a wide range of documentation training, principally at three levels: demonstration by the lecturer to the students; use by the students in the retrieval and dissemination of information; and development by the students through the selection and abstracting of documents, the indexing and storage of information and ultimately the use of feedback from the dissemination stage to improve the systems.

We know a great deal about the ways in which routines of news coverage may bias newspaper content, but little about how different article retrieval practices influence newspaper data assembled by scholars. Using the New York Times as a source of data on social movement activity, we compare depictions of protest by the African-American Civil Rights movement over time produced using the two most common article retrieval methods: index versus full-story coding. Full-story coding clearly offers more depth and greater breadth in terms of the events identified. Moreover, many of the same event characteristics associated with selection bias in newspaper reporting (e.g., size and confrontational nature of a protest event, presence of counter-demonstrators or police, and event sponsorship by a recognized social movement organization) are selected upon again when stories are indexed by New York Times staff.