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MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01

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Date: 20. 1.2007 18:30:22

MacFarlane, A.; McCann, J.A.; Robertson, S.E.: Parallel methods for the generation of partitioned inverted files (2005) 0.01

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Source: Aslib proceedings. 57(2005) no.5, S.434-459

MacFarlane, A.: Evaluation of web search for the information practitioner (2007) 0.01

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Source: Aslib proceedings. 59(2007) no.4/5, S.352-366

MacFarlane, A.; McCann, J.A.; Robertson, S.E.: Parallel methods for the update of partitioned inverted files (2007) 0.01

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Source: Aslib proceedings. 59(2007) no.4/5, S.367-396

Inskip, C.; MacFarlane, A.; Rafferty, P.: Meaning, communication, music : towards a revised communication model (2008) 0.01

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Source: Journal of documentation. 64(2008) no.5, S.687-706

Lu, W.; MacFarlane, A.; Venuti, F.: Okapi-based XML indexing (2009) 0.01

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Source: Aslib proceedings. 61(2009) no.5, S.483-499

Inskip, C.; MacFarlane, A.; Rafferty, P.: Organising music for movies (2010) 0.01

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Source: Aslib proceedings. 62(2010) nos.4/5, S.489-501

MacFarlane, A.; Missaoui, S.; Frankowska-Takhari, S.: On machine learning and knowledge organization in multimedia information retrieval (2020) 0.01
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Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).

Berget, G.; MacFarlane, A.: What Is known about the impact of impairments on information seeking and searching? (2020) 0.01

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Source: Journal of the Association for Information Science and Technology. 71(2020) no.5, S.596-611

MacFarlane, A.; Missaoui, S.; Makri, S.; Gutierrez Lopez, M.: Sender vs. recipient-orientated information systems revisited (2022) 0.00

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Date: 5. 6.2022 17:09:06

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