The impact of MIREX on scholarly research (2005-2010)

This paper explores the impact of the MIREX (Music Information Retrieval Evaluation eXchange) evaluation initiative on scholarly research. Impact is assessed through a bibliometric evaluation of both the MIREX extended abstracts and the papers citing the MIREX results, the trial framework and methodology, or MIREX datasets. Impact is examined through number of publications and citation analysis. We further explore the primary publication venues for MIREX results, the geographic distribution of both MIREX contributors and researchers citing MIREX results, and the spread of MIREX-based research beyond the MIREX contributor teams. This analysis indicates that research in this area is highly collaborative, has achieved an international dissemination, and has grown to have a significant profile in the research literature

Citation metrics for legal information retrieval: scholars and practitioners intertwined?

This paper examines citations in legal documents in the context of bibliometric-enhanced legal information retrieval. It is suggested that users of legal information retrieval systems wish to see both scholarly and non-scholarly information, and legal information retrieval systems are developed to be used by both scholarly and non-scholarly users. Since the use of citations in building arguments plays an important role in the legal domain, bibliometric information (such as citations) is an instrument to enhance legal information retrieval systems. This paper examines, through literature and data analysis, whether a bibliometric-enhanced ranking for legal information retrieval should consider both scholarly and non-scholarly publications, and whether this ranking could serve both user groups, or whether a distinction needs to be made.Our literature analysis suggests that for legal documents, there is no strict separation between scholarly and non-scholarly documents. There is no clear mark by which the two groups can be separated, and in as far as a distinction can be made, literature shows that both scholars and practitioners (non-scholars) use both types.We perform a data analysis to analyze this finding for legal information retrieval in practice, using citation and usage data from a legal search engine in the Netherlands. We first create a method to classify legal documents as either scholarly or non-scholarly based on criteria found in the literature. We then semi-automatically analyze a set of seed documents and register by what (type of) documents they are cited. This resulted in a set of 52 cited (seed) documents and 3086 citing documents. Based on the affiliation of users of the search engine, we analyzed the relation between user group and document type.Our data analysis confirms the literature analysis and shows much cross-citations between scholarly and non-scholarly documents. In addition, we find that scholarly users often open non-scholarly documents and vice versa. Our results suggest that for use in legal information retrieval systems citations in legal documents measure part of a broad scope of impact, or relevance, on the entire legal field. This means that for bibliometric-enhanced ranking in legal information retrieval, both scholarly and non-scholarly documents should be considered. The disregard by both scholarly and non-scholarly users of the distinction between scholarly and non-scholarly publications also suggests that the affiliation of the user is not likely a suitable factor to differentiate rankings on. The data in combination with literature suggests that a differentiation on user intent might be more suitable.Algorithms and the Foundations of Software technolog

BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fash-ion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA.Comment: 1

The scholarly impact of TRECVid (2003-2009)

This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams ‘scored’ highly against the evaluation criteria and the relationship between ‘top scoring’ teams at TRECVid and the ‘top scoring’ papers in terms of citations is analysed. A strong relationship was found between ‘success’ at TRECVid and ‘success’ at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed

The use of bibliometrics for assessing research : possibilities, limitations and adverse effects

Researchers are used to being evaluated: publications, hiring, tenure and funding decisions are all based on the evaluation of research. Traditionally, this evaluation relied on judgement of peers but, in the light of limited resources and increased bureaucratization of science, peer review is getting more and more replaced or complemented with bibliometric methods. Central to the introduction of bibliometrics in research evaluation was the creation of the Science Citation Index (SCI)in the 1960s, a citation database initially developed for the retrieval of scientific information. Embedded in this database was the Impact Factor, first used as a tool for the selection of journals to cover in the SCI, which then became a synonym for journal quality and academic prestige. Over the last 10 years, this indicator became powerful enough to influence researchers’ publication patterns in so far as it became one of the most important criteria to select a publication venue. Regardless of its many flaws as a journal metric and its inadequacy as a predictor of citations on the paper level, it became the go-to indicator of research quality and was used and misused by authors, editors, publishers and research policy makers alike. The h-index, introduced as an indicator of both output and impact combined in one simple number, has experienced a similar fate, mainly due to simplicity and availability. Despite their massive use, these measures are too simple to capture the complexity and multiple dimensions of research output and impact. This chapter provides an overview of bibliometric methods, from the development of citation indexing as a tool for information retrieval to its application in research evaluation, and discusses their misuse and effects on researchers’ scholarly communication behavior

Science Models as Value-Added Services for Scholarly Information Systems

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric

Popular and/or Prestigious? Measures of Scholarly Esteem

Citation analysis does not generally take the quality of citations into account: all citations are weighted equally irrespective of source. However, a scholar may be highly cited but not highly regarded: popularity and prestige are not identical measures of esteem. In this study we define popularity as the number of times an author is cited and prestige as the number of times an author is cited by highly cited papers. Information Retrieval (IR) is the test field. We compare the 40 leading researchers in terms of their popularity and prestige over time. Some authors are ranked high on prestige but not on popularity, while others are ranked high on popularity but not on prestige. We also relate measures of popularity and prestige to date of Ph.D. award, number of key publications, organizational affiliation, receipt of prizes/honors, and gender.Comment: 26 pages, 5 figure

Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.Comment: 2 pages, workshop paper accepted at the SIGIR 201

The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels

We investigate the similarities of pairs of articles which are co-cited at the different co-citation levels of the journal, article, section, paragraph, sentence and bracket. Our results indicate that textual similarity, intellectual overlap (shared references), author overlap (shared authors), proximity in publication time all rise monotonically as the co-citation level gets lower (from journal to bracket). While the main gain in similarity happens when moving from journal to article co-citation, all level changes entail an increase in similarity, especially section to paragraph and paragraph to sentence/bracket levels. We compare results from four journals over the years 2010-2015: Cell, the European Journal of Operational Research, Physics Letters B and Research Policy, with consistent general outcomes and some interesting differences. Our findings motivate the use of granular co-citation information as defined by meaningful units of text, with implications for, among others, the elaboration of maps of science and the retrieval of scholarly literature