1,720,998 research outputs found
RouterRetriever: Routing over a Mixture of Expert Embedding Models
No full textInformation retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reasonable overall performance, they often underperform models trained on domain-specific data when testing on their respective domains. Prior work in information retrieval has tackled this through multi-task training, but the idea of routing over a mixture of domain-specific expert retrievers remains unexplored despite the popularity of such ideas in language model generation research. In this work, we introduce RouterRetriever, a retrieval model that leverages a mixture of domain-specific experts by using a routing mechanism to select the most appropriate expert for each query. RouterRetriever is lightweight and allows easy addition or removal of experts without additional training. Evaluation on the BEIR benchmark demonstrates that RouterRetriever outperforms both models trained on MSMARCO (+2.1 absolute nDCG@10) and multi-task models (+3.2). This is achieved by employing our routing mechanism, which surpasses other routing techniques (+1.8 on average) commonly used in language modeling. Furthermore, the benefit generalizes well to other datasets, even in the absence of a specific expert on the dataset. RouterRetriever is the first work to demonstrate the advantages of routing over a mixture of domain-specific expert embedding models as an alternative to a single, general-purpose embedding model, especially when retrieving from diverse, specialized domains
Enriching Scientific Paper Embeddings with Citation Context
No full textThesis (Master's)--University of Washington, 2019Amid profusion of scientific literature, methods to organize and search available papers are quite valuable. Embedded representations of papers have potential to be used as input to a variety of tasks related to research paper search and recommendation. Such methods typically focus on document content, though some incorporate citation information. This citation information, however, is generally treated as fungible, with any citation given equal weight and identical meaning as any other. Recent advances in automated citation classifi- cation allow citations to be classified according how they are used in the citing document. I present a novel method for incorporating intent information into scientific paper embeddings through edge-weighting and concatenation of per-intent node2vec embeddings. Furthermore, I suggest that a hybrid approach, including both text and network data to generate embed- dings can take advantage of both complementary and reinforcing information to provide a fuller embedded representation. I evaluate these embeddings on a set of classification and sequence modeling tasks. The results show a significant improvement in some, but not all cases, suggesting that while the incorporation of citation intent classification into scientific paper embeddings is promising, further work is needed to assess whether it can out-perform state-of-the-art alternatives and to further elucidate its contributions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Text Summarization and Categorization for Scientific and Health-Related Data
No full textPh.D.The increasing amount of unstructured health-related data has created a need for intelligent processing, summarizing, and categorizing these data to extract knowledge from them. My research goal in this dissertation is to develop Natural Language Processing (NLP) and Information Retrieval (IR) methods for better processing and understanding health-related textual information to promote health care and wellbeing of individuals.First, I focus on scientific literature as an important source of knowledge distribution in health care. It has become a challenge for researchers to keep up with the increasing rate at which scientific findings are published. To address this problem, I propose summarization methods using citation texts and discourse structure of the papers to provide a concise representation of important contributions of the papers. I also investigate methods to address the problem of citation inaccuracy by linking the citations to their related parts in the target paper, capturing their relevant context. In addition, I raise the problem of the inadequacy of current evaluation metrics for scientific document summarization and present a superior method based on semantic relevance in evaluating the summaries.In the second part, I focus on other significant sources of health-related information including clinical notes and social media. I investigate categorization methods to address the critical problem of medical errors which are among leading causes of death worldwide. I demonstrate how we can effectively identify significant errors and harmful cases through medical narratives that could help prevent similar future problems. Mental health is another significant dimension of health and wellbeing that is sometimes overlooked. Suicide, the most serious challenge in mental health, accounts for approximately 1.4% of all deaths and approximately one person dies by suicide every 40 seconds. I investigate social media as a platform through which mental problems such as depression and self-harm can be investigated. I present both feature-rich and neural network methods for assessing the risk of depression, self-harm, and suicide to the individuals based on their general language expressed in social media
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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