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3 results for “Food Recognition”
Qualitative dataset - Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation (Smaal et al., 2021)
<p>This qualitative dataset contains the English translations of the plain texts of the urban food strategy documents or webpages of 16 European medium-sized cities: Basel [CH]; Bristol [UK]; Bruges [BE]; Cordoba [ES]; Donostia - San Sebastián [ES]; Ede [NL]; Geneva [CH]; Ghent [BE]; Grenoble [FR]; Groningen [NL]; Montpellier [FR]; Nantes [FR]; Rennes [FR]; Tours [FR]; Uppsala [SE]; and Vitoria-Gasteiz [ES]. The search for and translation of the urban food strategy documents and webpages have been performed in early 2019. The files have been analysed in NVivo (qualitative data analysis software). The upload also includes figures and a table with the authors' assessments connected to the resources and services codes and radar diagram visualisations presented in the following paper: </p> <p>Smaal, S. A. L., Dessein, J., Wind, B. J., & Rogge, E. (2021). Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation. <em>Agriculture and Human Values</em>, 38(3), 709–727. <a href="http://doi.org/10.1007/s10460-020-10179-6">https://doi.org/10.1007/s10460-020-10179-6</a> </p> <p><strong>Abstract: </strong>More and more cities develop urban food strategies (UFSs) to guide their efforts and practices towards more sustainable food systems. An emerging theme shaping these food policy endeavours, especially prominent in North and South America, concerns the enhancement of social justice within food systems. To operationalise this theme in a European urban food governance context we adopt Nancy Fraser’s three-dimensional theory of justice: economic redistribution, cultural recognition and political representation. In this paper, we discuss the findings of an exploratory document analysis of the social justice-oriented ambitions, motivations, current practices and policy trajectories articulated in sixteen European UFSs. We reflect on the food-related resource allocations, value patterns and decision rules these cities propose to alter and the target groups they propose to support, empower or include. Overall, we find that UFSs make little explicit reference to social justice and justice-oriented food concepts, such as food security, food justice, food democracy and food sovereignty. Nevertheless, the identified resources, services and target groups indicate that the three dimensions of Fraser are at the heart of many of the measures described. We argue that implicit, fragmentary and unspecified adoption of social justice in European UFSs is problematic, as it may hold back public consciousness, debate and collective action regarding food system inequalities and may be easily disregarded in policy budgeting, implementation and evaluation trajectories. As a path forward, we present our plans for the RE-ADJUSTool that would enable UFS stakeholders to reflect on how their UFS can incorporate social justice and who to involve in this pursuit.</p> <p><em>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389. </em></p> <p>Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a></p>
LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food
<p><strong>LivingNER Gold Standard corpus (includes training, validation, test and background sets + MULTILINGUAL RESOURCES</strong>)</p><p> </p><p><strong>Please cite if you use this dataset:</strong></p><p>A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources, <i>Procesamiento del Lenguaje Natural</i> (2022)</p><p>@article{amiranda2022nlp, title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources}, author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, year={2022} }</p><p> </p><p><i><strong>1. Introduction</strong></i></p><p>The LivingNER Gold Standard corpus is a collection of<strong> 2000 clinical case reports</strong> covering a <strong>broad range of medical specialities</strong>, i.e. infectious diseases (including Covid-19 cases), cardiology, neurology, oncology, dentistry, pediatrics, endocrinology, primary care, allergology, radiology, psychiatry, ophthalmology, urology, internal medicine, emergency and intensive care medicine, tropical medicine, and dermatology <strong>annotated with species</strong> [SPECIES] (including <strong>living organisms</strong> and <strong>microorganisms</strong>) and <strong>infectious diseases</strong> [ENFERMEDAD] mentions. Species mentions include many <strong>pathogens</strong> and infectious agents, but also <strong>food</strong>, allergens, <strong>pets</strong> or other species, taxonomic groups and organisms of clinical relevance. </p><p>The LivingNER corpus has also annotations of mentions of <strong>humans</strong> (tag HUMAN), including the patients itself, <strong>family members</strong>, healhcare professionals or other persons mentioned in the case reports. Thus it can be useful to extract family history information of patients or information about the social and healthcare personal environment and interactions.</p><p>All mentions have been exhaustively manually mapped by experts to their corresponding <a href="https://www.ncbi.nlm.nih.gov/taxonomy"><strong>NCBI Taxonomy</strong></a> identifiers. </p><p>It was used for the <a href="https://temu.bsc.es/livingner/">LivingNER</a> Shared Task on pathogens and living beings detection and normalization in Spanish medical documents, which was celebrated as part of IberLEF 2022.</p><p> </p><p><i><strong>2. Training, validation, test and background sets</strong></i></p><p>The training set is composed of 1000 clinical case reports. The validation set includes 500 clinical case reports with the same characteristics and the test set includes 485. The background set is a collection of around 13k unannotated case reports that were originally added to prevent manual annotations in the test set during the competition and to create a Silver Standard.</p><p><i><strong>2.1 Annotations format</strong></i></p><p>Annotations and text files are distributed separately. The texts are in plain text (.txt in UTF-8) format, while the annotations are are distributed in a tab-separated file (.tsv) file with one row per annotation:</p><p>- For <strong>subtask 1 (LivingNER-Species NER track)</strong>, the .tsv file has the following columns:</p><ul><li>filename: document name</li><li>mark: identifier mention mark</li><li>label: mention type (SPECIES or HUMAN)</li><li>off0: starting position of the mention in the document</li><li>off1: ending position of the mention in the document</li><li>span: textual span</li></ul><p> - For <strong>subtask 2 (LivingNER-Species Norm track)</strong>, the .tsv file has the same columns as the previous one, plus:</p><ul><li>isH: whether the span is narrower than the NCBITax assigned code</li><li>isN: whether the mention corresponds to a nosocomial infection</li><li>iscomplex: whether the span has assigned a combination of NCBITax codes</li><li>NCBITax: mention code in the NCBI Taxonomy</li></ul><p>- For <strong>subtask 3 (LivingNER-Clinical IMPACT track)</strong>, the .tsv file has the following columns:</p><ul><li>filename</li><li>isPet (Yes/No)</li><li>PetIDs (NCBITaxonomy codes of pet & farm animals present in document)</li><li>isAnimalInjury (Yes/No)</li><li>AnimalInjuryIDs (NCBITaxonomy codes of animals causing injuries present in document)</li><li>IsFood (Yes/No)</li><li>FoodIDs (NCBITaxonomy codes of food mentions present in document)</li><li>isNosocomial (Yes/No)</li><li>NosocomialIDs (NCBITaxonomy codes of nosocomial species mentions present in document)</li></ul><p><i><strong>2.2 Important notes about subtask 3 (LivingNER-Clinical IMPACT track):</strong></i></p><ul><li><strong>Less clinical case reports</strong>. Subtask 3 (LivingNER-Clinical IMPACT track) contains half of the clinical case reports (500 in the training partition, 250 in the validation partition). The list of valid clinical case reports for task 3 is included in the data (train_files_task3.txt and validation_files_task3.txt)</li><li><strong>Enriched dataset.</strong> The GS format is the one described above (a TSV with one line per clinical case report). However, we believe participants may find useful and <strong>enriched dataset. </strong>Then, we provide an additional dataset, with the mentions of the NER track classified in the 4 Clinical impact categories (food, pet&farm animals, animals causing injuries and nosocomial). It is a TSV file with one row per annotation, and with the following columns: filename, mark, label, off0, off1, span, isPet, isAnimalInjury, isFood, isNosocomial, isH, iscomplex, code</li></ul><p> </p><p><i><strong>3. Multilingual resources</strong></i></p><p>We have generated the annotated training and validation sets in <strong>7 languages</strong>:</p><ul><li><i><strong>English</strong></i></li><li><i><strong>Portuguese</strong></i></li><li><i><strong>Catalan</strong></i></li><li><i><strong>Galician</strong></i></li><li><i><strong>Italian</strong></i></li><li><i><strong>French</strong></i></li><li><i><strong>Romanian</strong></i></li></ul><p> </p><p>The process was:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using an annotation transfer technology.</li></ol><p>The text files are stored in the multilingual_resources/<strong>training-text-files</strong> and multilingual_resources/<strong>validation-text-files </strong>subfolders.</p><p>The annotated TSV files are stored in the multilingual_resources/<strong>annotation_transfer </strong>subfolder.</p><p>For the sake of comparison, we incorporate as well the annotations that resulted from the <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85">LINNAEUS tool</a> in the multilingual_resources/<strong>linneaus</strong> subfolder.</p><p>If you want to visualize the multilingual resources, check out this Brat server: <a href="https://temu.bsc.es/mLivingNER/#/translations/">https://temu.bsc.es/mLivingNER/#/translations/</a></p><p>For instance, you can see the parallel annotations in <a href="https://temu.bsc.es/mLivingNER/diff.xhtml#/translations/en/annotation_transfer/train/casos_clinicos_cardiologia34?diff=/translations/fr/annotation_transfer/train/">English vs in French</a>, or <a href="https://temu.bsc.es/mLivingNER/diff.xhtml#/translations/cat/annotation_transfer/train/casos_clinicos_cardiologia35?diff=/gold-standard/train/">in Spanish (the gold standard) vs in Catalan.</a></p><p> </p><p><strong>Resources</strong></p><ul><li><a href="https://temu.bsc.es/livingner/"><strong>Task Web</strong></a></li><li><strong>Citation: </strong>A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources, <i>Procesamiento del Lenguaje Natural</i> (2022)</li><li><a href="https://doi.org/10.5281/zenodo.6385162"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/tonifuc3m/livingner-evaluation-library"><strong>Evaluation library</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.6390506">LivingNER terminology</a></li><li><a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6444"><strong>Overview paper</strong></a></li><li><a href="https://ceur-ws.org/Vol-3202/"><strong>Proceedings participant papers</strong></a></li><li><a href="https://www.youtube.com/watch?v=8VcZw8ywyJY&list=PL5uSCzf1azhA_gMLC3DBZe6NvmMJiggTg"><strong>Youtube videos</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/mention-detection-normalization-classification-of-species-pathogens-humans-and-food-in-clinical-documents-overview-of-the-livingner-shared-task-and-resources-talk-at-iberlef-sepln-2022"><strong>LivingNER overview talk sides at IberLEF/SEPLN</strong></a></li></ul><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, sign and findings mentions and normalization to SNOMED CT, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), differentdocument collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, different document collection, some overlapping documents)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, different document collection, some overlapping documents)</li></ul>
Photographic Food Recognition and Meal Size Estimation Before and After Roux-en-Y Gastric Bypass
ClinicalTrials.gov study NCT04600596. IPD Sharing: NO. Countries: 1. Publications: 20.
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