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Conventional studies mainly classify a term’s appearance in the retrieved documents as either relevant or irrelevant for application. The purpose of this paper is to differentiate the term’s appearances in the retrieved documents in more detailed situations to generate relevance information and demonstrate the applicability of the derived information in combination with current methods of query expansion.

A method was designed first to utilize the derived information owing to term appearance differentiation within a conventional query expansion approach that has been proven as an effective technology in the enhancement of information retrieval. Then, an information retrieval system was developed to demonstrate the realization and sustain the study of the method. Formal tests were conducted to examine the distinguishing capability of the proposed information utilized in the method.

The experimental results show that substantial differences in performances can be achieved between the proposed method and the conventional query expansion method alone.

Since the proposed information resides at the bottom of the information hierarchy of relevance feedback, any technology regarding the application of relevance feedback information could consider the utilization of this piece of information.

The importance of the study is the disclosure of the applicability of the proposed information beyond current usage of term appearances in relevant/irrelevant documents and the initiation of a query expansion technology in the application of this information.

The purpose of this paper is to identify distinguishing term characteristics from among the information of term appearance situations (tas) residing in the relevant/irrelevant documents retrieved for use. Terms with specific characteristics could be used in the distinguishing of user profiles, documents, pages or concepts to assist in information retrieval.

First, a method to apply the potential term characteristics in the distinguishing of user profiles in the information retrieval environment is designed. Then, an information retrieval system is developed to demonstrate the realisation and sustain the study of the method. Formal tests are conducted to examine the distinguishing capability of the potential term characteristics proposed in the method.

The results of the tests show that the potential term characteristics proposed in this study are successfully applied in the distinguishing of user profiles in the information retrieval environment.

Identification of distinguishing term characteristics would expand the ground for the IR community in the design of feature‐extraction algorithms or systems that try to cull information from structured or unstructured documents.

The purpose of this paper is to establish a new approach for solving the expansion term problem.

This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.

The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.

The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.

Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.