Term and citation retrieval: A field study

The relative efficacy of searching by terms and by citations is investigated with real searches collected in health sciences libraries. The objective is to seek evidence to confirm or refute findings from a controlled pilot study, and to understand the factors at work in operational search environments. Overall confirmation was found. In both the pilot and field studies, the improvement of the odds that overlap items retrieved would be relevant or partially relevant was truly astounding. If an item was retrieved from both MEDLINE(R) and SCISEARCH(R), it was six times more likely that it would be relevant or partially relevant as opposed to being not relevant, and 8.4 times more likely for definitely relevant retrievals. In the field setting, citation searching was able to add an average of 24% recall to traditional subject retrieval. Term or citation searching from the open literature produced lower precision results. Attempts to identify distinguishing characteristics in queries which might benefit most from additional citation searches proved to be inconclusive. In spite of the obvious gain shown by citation searching, online access of citation databases has been hampered by their relative high cost.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31048/1/0000725.pd

LAW SEARCH IN THE AGE OF THE ALGORITHM

The process of searching for relevant legal materials is fundamental to legal reasoning. However, despite its enormous practical and theoretical importance, law search has not been given significant attention by scholars. In this Article, we define the problem of law search and examine the consequences of new technologies capable of automating this core lawyerly task. We introduce a theory of law search in which legal relevance is a sociological phenomenon that leads to convergence over a shared set of legal materials and explore the normative stakes of law search. We examine ways in which law scholars can understand empirically the phenomenon of law search, argue that computational modeling is a valuable epistemic tool in this domain, and report the results from a multi-year, interdisciplinary effort to develop an advanced law search algorithm based on human-generated data. Finally, we explore how policymakers can manage the challenges posed by new machine learning-based search technologies

An investigation into weighted data fusion for content-based multimedia information retrieval

Content Based Multimedia Information Retrieval (CBMIR) is characterised by the combination of noisy sources of information which, in unison, are able to achieve strong performance. In this thesis we focus on the combination of ranked results from the independent retrieval experts which comprise a CBMIR system through linearly weighted data fusion. The independent retrieval experts are low-level multimedia features, each of which contains an indexing function and ranking algorithm. This thesis is comprised of two halves. In the first half, we perform a rigorous empirical investigation into the factors which impact upon performance in linearly weighted data fusion. In the second half, we leverage these finding to create a new class of weight generation algorithms for data fusion which are capable of determining weights at query-time, such that the weights are topic dependent

Validating and Developing the User Engagement Scale in Web-based Visual Information Searching

Guided by the theoretical frameworks of interactive information searching and user engagement (UE), this study proposed sense discovery (SD) as a UE attribute and suggested a refined four-factor user engagement scale (UES) model for the measurement of users’ psychological involvement in web-based visual information searching. Using a mixed-methods approach based on a survey, this study confirmed the inter-item reliability of the original six-factor UES in three visual contexts—a general visual context, image searching on Google (ISG), and video searching on YouTube (VSY). Principal component analyses (PCA) partially confirmed the internal consistency of the original six UE subscales and suggested conceptual overlaps among four of six original subscales. Through thematic and sentiment analyses of the participants’ visual information needs, the study further explored their positive experience and categorized a total of eight items related to SD. Based on the findings, a refined four-factor UES model, which can be flexibly administered, is proposed to measure users’ psychological involvement in web-based visual information searching

From Classification to Indexing: How Automation Transforms the Way we Think

To classify is to organize the particulars in a body of information according to some meaningful scheme. Difficulty recognizing metaphor, synonyms and homonyms, and levels of generalization renders those applications of artificial intelligence that are currently in widespread use at a loss to deal effectively with classification. Indexing conveys nothing about relationships; it pinpoints information on particular topics without reference to anything else. Keyword searching is a form of indexing, and here artificial intelligence excels. Growing reliance on automated means of accessing information brings an increase in indexing and a corresponding decrease in classification. This brings about a shift from the modernist view of the world as permanently and hierarchically structured to the indeterminacy and contingency associated with postmodernism

Environmental sciences research in northern Australia, 2000-2011: a bibliometric analysis within the context of a national research assessment exercise

This paper reports on a bibliometric analysis of environmental sciences research in northern Australia between 2000 and 2011. It draws on publications data for Charles Darwin University (CDU) and James Cook University (JCU) researchers to present a bibliometric profile of the journals in which they publish, the citations to their research outputs, and the key research topics discussed in the publications. Framing this analysis, the study explored the relationship between the two universities’ publications and their ‘fit’ with the environmental sciences field as defined by the Australian research assessment model, Excellence in Research for Australia (ERA). The Scopus database retrieved more records than Web of Science, although only minor differences were seen in the journals in which researchers published most frequently and the most highly cited articles. Strong growth in publications is evident in the 12 year period, but the journals in which the researchers publish most frequently differ from the journals in which the most highly cited articles are published. Many of the articles by CDU and JCU affiliated researchers are published in journals outside of the environmental sciences category as defined by Scopus and Web of Science categories and the ERA, however, the research conducted at each university aligns closely with that institution’s research priorities

Learning Analytics for Academic Writing through Automatic Identification of Meta-discourse

Effective written communication is an essential skill which promotes educational success for undergraduates. Argumentation is a key requirement of successful writing, which is the most common genre that undergraduates have to write particularly in the social sciences. Therefore, when assessing student writing academic tutors look for students’ ability to present and pursue well-reasoned and strong arguments through scholarly argumentation, which is articulated by meta-discourse. Today, there are some natural language processing systems which automatically detect authors’ rhetorical moves in scholarly texts. Hence, when assessing their students’ essays, educators could benefit from the available automated textual analysis which can detect meta-discourse. However, previous work has not shown whether these technologies can be used to analyse student writing reliably. The aim of this thesis therefore has been to understand how automated analysis of meta-discourse in student writing can be used to support tutors’ essay assessment practices. This thesis evaluates a particular language analysis tool, the Xerox Incremental Parser (XIP) as an exemplar of this type of automated technology. The studies presented in this thesis investigates how tutors define the quality of undergraduate writing and suggests key elements that make for good quality student writing in the social sciences, where XIP seems to work best. This thesis also sets out the changes that needs to be made to the XIP and proposes in what ways its output can be delivered to tutors so that they make use of this output to give feedback on student essays. The findings reported also show problems that academic tutors experience in essay assessment, which potentially could be solved by automated support. However, tutors have preconceptions about the use of automated support. The study revealed that tutors want to be assured that they retain the ‘power’ themselves in any decision of using automated support to overcome these preconceptions

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives