Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

Toward higher effectiveness for recall-oriented information retrieval: A patent retrieval case study

Research in information retrieval (IR) has largely been directed towards tasks requiring high precision. Recently, other IR applications which can be described as recall-oriented IR tasks have received increased attention in the IR research domain. Prominent among these IR applications are patent search and legal search, where users are typically ready to check hundreds or possibly thousands of documents in order to find any possible relevant document. The main concerns in this kind of application are very different from those in standard precision-oriented IR tasks, where users tend to be focused on finding an answer to their information need that can typically be addressed by one or two relevant documents. For precision-oriented tasks, mean average precision continues to be used as the primary evaluation metric for almost all IR applications. For recall-oriented IR applications the nature of the search task, including objectives, users, queries, and document collections, is different from that of standard precision-oriented search tasks. In this research study, two dimensions in IR are explored for the recall-oriented patent search task. The study includes IR system evaluation and multilingual IR for patent search. In each of these dimensions, current IR techniques are studied and novel techniques developed especially for this kind of recall-oriented IR application are proposed and investigated experimentally in the context of patent retrieval. The techniques developed in this thesis provide a significant contribution toward evaluating the effectiveness of recall-oriented IR in general and particularly patent search, and improving the efficiency of multilingual search for this kind of task

Meaning refinement to improve cross-lingual information retrieval

Magdeburg, Univ., Fak. für Informatik, Diss., 2012von Farag Ahme

The Evolution of Sociology of Software Architecture

The dialectical interplay of technology and sociological development goes back to the early days of human development, starting with stone tools and fire, and coming through the scientific and industrial revolutions; but it has never been as intense or as rapid as in the modern information age of software development and accelerating knowledge society (Mansell and Wehn, 1988; and Nico, 1994, p. 1602-1604). Software development causes social change, and social challenges demand software solutions. In turn, software solutions demand software application architecture. Software architecture (“SA”) (Fielding and Taylor, 2000) is a process for “defining a structural solution that meets all the technical and operations requirements...” (Microsoft, 2009, Chapter I). In the SA process, there is neither much emphasis on the sociological requirements of all social stakeholders nor on the society in w hich these stakeholders use, operate, group, manage, transact, dispute, and resolve social conflicts. For problems of society demanding sociological as well as software solutions, this study redefines software application architecture as “the process of defining a structured solution that meets all of the sociological , technical, and operational requirements…” This investigation aims to l ay the groundwork for, evolve, and develop an innovative and novel sub-branch of scientific study we name the “Sociology of Software Architecture” (hereinafter referred to as “SSA”). SSA is an interdisciplinary and comparative study integrating, synthesizing, and combining elements of the disciplines of sociology, sociology of technology, history of technology, sociology of knowledge society, epistemology, science methodology (philosophy of science), and software architecture. Sociology and technology have a strong, dynamic, and dialectical relationship and interplay, especially in software development. This thesis investigates and answers important and relevant questions, evolves and develops new scientific knowledge, proposes solutions, demonstrates and validates its benefits, shares its case studies and experiences, and advocates, promotes, and helps the future and further development of this novel method of science

The usability of software for authoring and editing

This research investigates some of the reasons for the reported difficulties experienced by writers when using editing software designed for structured documents. The overall objective was to determine if there are aspects of the software interfaces which militate against optimal document construction by writers who are not computer experts, and to suggest possible remedies. Studies were undertaken to explore the nature and extent of the difficulties, and to identify which components of the software interfaces are involved. A model of a revised user interface was tested, and some possible adaptations to the interface are proposed which may help overcome the difficulties. The methodology comprised: 1. identification and description of the nature of a ‘structured document’ and what distinguishes it from other types of document used on computers; 2. isolation of the requirements of users of such documents, and the construction a set of personas which describe them; 3. evaluation of other work on the interaction between humans and computers, specifically in software for creating and editing structured documents; 4. estimation of the levels of adoption of the available software for editing structured documents and the reactions of existing users to it, with specific reference to difficulties encountered in using it; 5. examination of the software and identification of any mismatches between the expectations of users and the facilities provided by the software; 6. assessment of any physical or psychological factors in the reported difficulties experienced, and to determine what (if any) changes to the software might affect these. The conclusions are that seven of the twelve modifications tested could contribute to an improvement in usability, effectiveness, and efficiency when writing structured text (new document selection; adding new sections and new lists; identifying key information typographically; the creation of cross-references and bibliographic references; and the inclusion of parts of other documents). The remaining five were seen as more applicable to editing existing material than authoring new text (adding new elements; splitting and joining elements [before and after]; and moving block text)

Term selection in information retrieval

Systems trained on linguistically annotated data achieve strong performance for many language processing tasks. This encourages the idea that annotations can improve any language processing task if applied in the right way. However, despite widespread acceptance and availability of highly accurate parsing software, it is not clear that ad hoc information retrieval (IR) techniques using annotated documents and requests consistently improve search performance compared to techniques that use no linguistic knowledge. In many cases, retrieval gains made using language processing components, such as part-of-speech tagging and head-dependent relations, are offset by significant negative effects. This results in a minimal positive, or even negative, overall impact for linguistically motivated approaches compared to approaches that do not use any syntactic or domain knowledge. In some cases, it may be that syntax does not reveal anything of practical importance about document relevance. Yet without a convincing explanation for why linguistic annotations fail in IR, the intuitive appeal of search systems that ‘understand’ text can result in the repeated application, and mis-application, of language processing to enhance search performance. This dissertation investigates whether linguistics can improve the selection of query terms by better modelling the alignment process between natural language requests and search queries. It is the most comprehensive work on the utility of linguistic methods in IR to date. Term selection in this work focuses on identification of informative query terms of 1-3 words that both represent the semantics of a request and discriminate between relevant and non-relevant documents. Approaches to word association are discussed with respect to linguistic principles, and evaluated with respect to semantic characterization and discriminative ability. Analysis is organised around three theories of language that emphasize different structures for the identification of terms: phrase structure theory, dependency theory and lexicalism. The structures identified by these theories play distinctive roles in the organisation of language. Evidence is presented regarding the value of different methods of word association based on these structures, and the effect of method and term combinations. Two highly effective, novel methods for the selection of terms from verbose queries are also proposed and evaluated. The first method focuses on the semantic phenomenon of ellipsis with a discriminative filter that leverages diverse text features. The second method exploits a term ranking algorithm, PhRank, that uses no linguistic information and relies on a network model of query context. The latter focuses queries so that 1-5 terms in an unweighted model achieve better retrieval effectiveness than weighted IR models that use up to 30 terms. In addition, unlike models that use a weighted distribution of terms or subqueries, the concise terms identified by PhRank are interpretable by users. Evaluation with newswire and web collections demonstrates that PhRank-based query reformulation significantly improves performance of verbose queries up to 14% compared to highly competitive IR models, and is at least as good for short, keyword queries with the same models. Results illustrate that linguistic processing may help with the selection of word associations but does not necessarily translate into improved IR performance. Statistical methods are necessary to overcome the limits of syntactic parsing and word adjacency measures for ad hoc IR. As a result, probabilistic frameworks that discover, and make use of, many forms of linguistic evidence may deliver small improvements in IR effectiveness, but methods that use simple features can be substantially more efficient and equally, or more, effective. Various explanations for this finding are suggested, including the probabilistic nature of grammatical categories, a lack of homomorphism between syntax and semantics, the impact of lexical relations, variability in collection data, and systemic effects in language systems