Peter Bruza
Queensland University of Technology, Information Systems, Faculty Member
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Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of... more
Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to influence future algorithm development to better meet the requirements of qualitative content analysts.
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ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive... more
ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive science showing that decision theory based on quantum probabilities is superior to modelling human judgements than standard probability models [2, 1]. By quantum probabilities, we mean decision event space is modelled as vector space rather than the usual Boolean algebra of sets. In this way,incompatible perspectives around a decision can be modelled leading to an interference term which modifies the law of total probability. The interference term is crucial in modifying the probability judgements made by current probabilistic systems so they align better with human judgement. The goal of this article is thus to model the information seeker user as a decision maker. For this purpose, signal detection models will be sketched which are in principle applicable in a wide variety of information seeking scenarios.
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The context-sensitivity of cognition has been demonstrated across a wide range of cognitive functions such as perception, memory, judgement and decision making. A related term, ‘contextuality’, has appeared from the field of quantum... more
The context-sensitivity of cognition has been demonstrated across a wide range of cognitive functions such as perception, memory, judgement and decision making. A related term, ‘contextuality’, has appeared from the field of quantum cognition, with mounting empirical evidence demonstrating that cognitive phenomena are sometimes contextual. Contextuality is a subtle notion that influences how we must view the properties of the cognitive phenomenon being studied. This article addresses the questions: What does it mean for a cognitive phenomenon to be contextual? What are the implications of contextuality for probabilistic models of cognition? How does contextuality differ from context-sensitivity? Starting from George Boole’s “conditions of possible experience”, we argue that a probabilistic model of a cognitive phenomenon is necessarily subject to an assumption of realism. By this we mean that the phenomenon being studied is assumed to have cognitive properties with a definite value ...
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We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so.... more
We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.
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Research Interests: Psychology, Cognitive Psychology, Cognitive Science, Computer Science, Semantics, and 15 moreWorking Memory, Semantic Memory, Priming, Medicine, Episodic Memory, Semantic Networks, Humans, Cues, Word Recognition, Association, Activation, Repetition Priming, Recall, Short Term Memory, and Neurosciences
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Research Interests: Computer Science, Information Retrieval, Artificial Intelligence, Ontology, Natural Language Processing, and 10 moreTheory, Semantic similarity, Neural Network, General Terms, Experimentation, Language Model, Unified Medical Language System, Artificial Neural Network, Medical Information Retrieval, and Similarity Geometry
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On the relevance of documents for semantic representation Laurianne Sitbon National ICT Australia Queensland University of Technology Brisbane, Australia laurianne. sitbon@ nicta. com. au Peter Bruza Queensland University of Technology... more
On the relevance of documents for semantic representation Laurianne Sitbon National ICT Australia Queensland University of Technology Brisbane, Australia laurianne. sitbon@ nicta. com. au Peter Bruza Queensland University of Technology Brisbane, Australia p. bruza@ qut. edu. au ...
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Research Interests: Psychology, Cognitive Science, Applied Mathematics, Computer Science, Information Retrieval, and 15 moreHuman Memory, Mathematical Psychology, Cognition, Quantum Theory, Medicine, Information Processing, Quantum entanglement, Lexicon, Mental Lexicon, Human Cognition, Mathematical, Abductive Reasoning, Contextuality, Semantic Structure, and quantum interaction
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Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much... more
Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, “contextuality,” is away to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, “quantum entanglement,” allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision