Teaching archive skills: a pedagogical journey with impact

This article considers the pedagogical practice of the Special Collections staff team at the University of Sussex and the impact of the group visit experience on student learning. It addresses our current group visit teaching offer to students at the University of Sussex and our move to a more student-led active learning approach. It considers the use of the ‘pedagogical toolkit’ including technology within the classroom, and the creation of a document identification form to encourage critical thinking. Our aim for any group visit is to provide a positive first experience and get students enthused about using archives. In 2017 we undertook our own impact study, detailed within the article, to follow the student journey with the intention of finding out if students returned to use archives for their studies as a result of their group visit. Moving forward, this article considers our future activities in response to the impact study and institutional initiatives

Modeling Documents as Mixtures of Persons for Expert Finding

In this paper we address the problem of searching for knowledgeable persons within the enterprise, known as the expert finding (or expert search) task. We present a probabilistic algorithm using the assumption that terms in documents are produced by people who are mentioned in them.We represent documents retrieved to a query as mixtures of candidate experts language models. Two methods of personal language models extraction are proposed, as well as the way of combining them with other evidences of expertise. Experiments conducted with the TREC Enterprise collection demonstrate the superiority of our approach in comparison with the best one among existing solutions

Question-answering, relevance feedback and summarisation : TREC-9 interactive track report

In this paper we report on the effectiveness of query-biased summaries for a question-answering task. Our summarisation system presents searchers with short summaries of documents, composed of a series of highly matching sentences extracted from the documents. These summaries are also used as evidence for a query expansion algorithm to test the use of summaries as evidence for interactive and automatic query expansion

Incorporating user search behaviour into relevance feedback

In this paper we present five user experiments on incorporating behavioural information into the relevance feedback process. In particular we concentrate on ranking terms for query expansion and selecting new terms to add to the user's query. Our experiments are an attempt to widen the evidence used for relevance feedback from simply the relevant documents to include information on how users are searching. We show that this information can lead to more successful relevance feedback techniques. We also show that the presentation of relevance feedback to the user is important in the success of relevance feedback