1,720,963 research outputs found
Background linking: Joining entity linking with learning to rank models
No full textThe recent years have been characterized by a strong democratization of news production on the web. In this scenario it is rare to find self-contained news articles that provide useful background and context information. The problem of finding information providing context to news articles has been tackled by the Background Linking task of the TREC News Track. In this paper, we propose a system to address the background linking task. Our system relies on LambdaMART learning to rank algorithm trained on classic textual features and on entity-based features. The idea is that the entities extracted from the documents as well as their relationships provide valuable context to the documents. We analyzed how this idea can be used to improve the effectiveness of (re-)ranking methods for the background linking task
MedTAG: a portable and customizable annotation tool for biomedical documents
Full text linkBackground: Semantic annotators and Natural Language Processing (NLP) methods for Named Entity Recognition and Linking (NER+L) require plenty of training and test data, especially in the biomedical domain. Despite the abundance of unstructured biomedical data, the lack of richly annotated biomedical datasets poses hindrances to the further development of NER+L algorithms for any effective secondary use. In addition, manual annotation of biomedical documents performed by physicians and experts is a costly and time-consuming task. To support, organize and speed up the annotation process, we introduce MedTAG, a collaborative biomedical annotation tool that is open-source, platform-independent, and free to use/distribute. Results: We present the main features of MedTAG and how it has been employed in the histopathology domain by physicians and experts to annotate more than seven thousand clinical reports manually. We compare MedTAG with a set of well-established biomedical annotation tools, including BioQRator, ezTag, MyMiner, and tagtog, comparing their pros and cons with those of MedTag. We highlight that MedTAG is one of the very few open-source tools provided with an open license and a straightforward installation procedure supporting cross-platform use. Conclusions: MedTAG has been designed according to five requirements (i.e. available, distributable, installable, workable and schematic) defined in a recent extensive review of manual annotation tools. Moreover, MedTAG satisfies 20 over 22 criteria specified in the same study
Reproducibility and Analysis of Scientific Dataset Recommendation Methods
Full text linkDatasets play a central role in scholarly communications. However, scholarly graphs are often incomplete, particularly due to the lack of connections between publications and datasets. Therefore, the importance of dataset recommendation—identifying relevant datasets for a scientific paper, an author, or a textual query—is increasing. Although various methods have been proposed for this task, their reproducibility remains unexplored, making it difficult to compare them with new approaches. We reviewed current recommendation methods for scientific datasets, focusing on the most recent and competitive approaches, including an SVM-based model, a bi-encoder retriever, a method leveraging co-authors and citation network embeddings, and a heterogeneous variational graph autoencoder. These approaches underwent a comprehensive analysis under consistent experimental conditions. Our reproducibility efforts show that three methods can be reproduced, while the graph variational autoencoder is challenging due to unavailable code and test datasets. Hence, we re-implemented this method and performed a component-based analysis to examine its strengths and limitations. Furthermore, our study indicated that three out of four considered methods produce subpar results when applied to real-world data instead of specialized datasets with ad-hoc features
Tracing Data Footprints: Formal and Informal Data Citations in the Scientific Literature
No full textData citation has become a prevalent practice within the scientific community, serving the purpose of facilitating data discovery, reproducibility, and credit attribution. Consequently, data has gained significant importance in the scholarly process. Despite its growing prominence, data citation is still at an early stage, with considerable variations in practices observed across scientific domains. Such diversity hampers the ability to consistently analyze, detect, and quantify data citations. We focus on the European Marine Science (MES) community to examine how data is cited in this specific context. We identify four types of data citations: formal, informal, complete, and incomplete. By analyzing the usage of these diverse data citation modalities, we investigate their impact on the widespread adoption of data citation practices
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
SKET: an Unsupervised Knowledge Extraction Tool to Empower Digital Pathology Applications
Full text linkLarge volumes of medical data have been produced for decades. These data include diagnoses, which are often reported as free text, thus encoding medical knowledge that is still largely unexploited. To decode the medical knowledge present within reports, we propose the Semantic Knowledge Extractor Tool (SKET), an unsupervised knowledge extraction system combining a rule-based expert system with pretrained Machine Learning (ML) models. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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