Linking Entities Across Images and Text

This paper describes a set of methods to link entities across images and text. As a corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category. We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels. We also measured the statistical associations between these mentions and the labels and we combined them with the semantic similarity to produce mappings in the form of pairs consisting of a region label and a caption entity. In a second step, we used the syntactic relationships between the mentions and the spatial relationships between the regions to rerank the lists of candidate mappings. To evaluate our methods, we annotated a test set of 200 images, where we manually linked the im- age regions to their corresponding mentions in the captions. Eventually, we could match objects in pictures to their correct mentions for nearly 89 percent of the segments, when such a matching exists

Visual Entity Linking: A Preliminary Study

In this paper, we describe a system that jointly extracts entities appearing in images and mentioned in their ac- companying captions. As input, the entity linking pro- gram takes a segmented image together with its cap- tion. It consists of a sequence of processing steps: part- of-speech tagging, dependency parsing, and coreference resolution that enables us to identify the entities as well as possible textual relations from the captions. The pro- gram uses the image regions labelled with a set of pre- defined categories and computes WordNet similarities between these labels and the entity names. Finally, the program links the entities it detected across the text and the images. We applied our system on the Segmented and Annotated IAPR TC-12 dataset that we enriched with entity annotations and we obtained a correct as- signment rate of 55.48

Problems with Sleep Do Not Predict Self-Reported Driving Factors and Perception in Older Drivers: Evidences from the Candrive II Prospective Cohort

Given that sleep problems and serious motor vehicle collisions are increasingly prevalent in older adults, even minor drowsiness could potentially contribute to driving patterns in older drivers. To date, it is unknown whether less serious problems with sleep influence driving frequency and ability in older adults. We investigated the influence of everyday sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Selfreported measures of sleep problems were used to investigate the influence of sleep disturbance on self-reported driving practices and perceived driving abilities. On two measures of self-reported driving outcomes, participants with problems with rated themselves more poorly. However, this relationship disappeared when health and demographic variables were entered prior in hierarchical regression analyses. Our results show that the relationship between sleep problems, driving frequency and perceived abilities is better explained by mediating demographic, health, and cognitive factors

Introduction

Welcome to the first issue of Undercurrents. This journal is the initiative of a group of graduate students at the Faculty of Environmental Studies at York University. Our motivation to publish a journal comes from a realization of the need for a forum to present critical and innovative graduate student work in the area of environmental studies. This, however, is an area of great diversity and a glance at the table of contents may leave some readers re-examining their assumptions about their notion of "environment." Indeed, it is the philosophy of Undercurrents to pursue the widest possible understanding of environment

Agrupaciones para la extracción de entidades clínicas

Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical entity recognition; identifying, for example, drugs, disorders, and body parts in clinical text. However, most of this work has focused on records in English. Therefore, this work aims to improve clinical entity recognition for languages other than English by comparing the same methods on two different languages, specifically by employing ensemble methods. Models were created for Spanish and Swedish health records using SVM, Perceptron, and CRF and four different feature sets, including unsupervised features. Finally, the models were combined in ensembles. Weighted voting was applied according to the models individual F-scores. In conclusion, the ensembles improved the overall performance for Spanish and the precision for Swedish.Los informes médicos son una valiosa fuente de conocimiento clínico. Las técnicas de Procesamiento del Lenguaje Natural han sido aplicadas al procesamiento de informes médicos para diversas aplicaciones. Generalmente un primer paso es la detección de entidades médicas: identificar medicamentos, enfermedades y partes del cuerpo. Sin embargo, la mayoría de los trabajos se han desarrollado para informes en Inglés. El objetivo de este trabajo es mejorar el reconocimiento de entidades médicas para otras lenguas diferentes a Inglés, comparando los mismos métodos en dos lenguas y utilizando agrupaciones de modelos. Los modelos han sido creados para informes médicos en Español y Sueco utilizando SVM, Perceptron, CRF y cuatro conjuntos diferentes de atributos, incluyendo atributos no supervisados. Para el modelo combinado se ha aplicado votación ponderada teniendo en cuenta la F-measure individual. En conclusión, el modelo combinado mejora el rendimiento general y para posibles mejoras debemos investigar métodos más sofisticados de agrupación.This work has been partially funded by the Spanish ministry (PROSAMED: TIN2016-77820-C3-1-R, TADEEP: TIN2015-70214-P), the Basque Government (DETEAMI: 2014111003), the University of the Basque Country UPV-EHU (MOV17/14) and the Nordic Center of Excellence in Health-Related e-Sciences (NIASC)

The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery

BACKGROUND: In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). METHODS: Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. RESULTS: From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. CONCLUSIONS: The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources

One Man\u27s Dog piece on the Maine Mouse-ah, a new mousetrap invented by three m

One Man\u27s Dog piece on the Maine Mouse-ah, a new mousetrap invented by three men in Hanover. More than 4,000 mousetraps have been registered with the U.S. Patent Office

The federal Centers for Disease Control recommends boiling fiddleheads for at le

The federal Centers for Disease Control recommends boiling fiddleheads for at least ten minutes, or steaming them for 20 minutes, citing raw or undercooked fiddleheads as the culprit in food poisoning cases. Chris Toole of the Harraseeket Inn of Freeport says the restaurant serves between 100 and 150 pounds of fiddleheads each spring, and has never heard a complaint of food poisoning. Dave Dzurec of the University of Maine Cooperative Extension Service wonders if the cases of food poisoning were caused by a source other than the fiddleheads, such as mold growth or bacterial contamination

The fishing industry is becoming impatient over Gov. Angus King\u27s delay in repla

The fishing industry is becoming impatient over Gov. Angus King\u27s delay in replacing Robin Alden, who resigned last November as the commisssoner of the Department of Marine Resources. Acting Commissioner Penn Estabrook can serve no more than six months, and must be replaced by May 10. The most likely choice for another acting commissioner is Joe Fessenden, colonel of the Marine Patrol. A search committee reached consensus on George Lapointe, who works for the Atlantic States Marine Fisheries Council, but King is reluctant to hire him because of his lack of familiarity with Maine

John Brinda of Washington state this month completed the 500-mile extension to t

John Brinda of Washington state this month completed the 500-mile extension to the Appalachian Trail. Because much of the International Appalachian Trail\u27s Maine extension is still in the planning stage, Brinda did not follow the proposed route. Brinda, the first hiker to complete the 500-mile extension, said extending the AT north seems like a natural development