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7 results for “Entity Extraction”
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
<p><strong>Datasets</strong></p> <ol> <li>DISTANT-CTO is a weakly-labelled dataset of 'Intervention' and 'Comparator' entity annotated sentences. The dataset was obtained using candidate generation the approach described in "DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low Resource Entity Extraction Using Clinical Trials Literature". <ol> <li>distantcto_high_conf.txt - ds conf 1.0 (full dataset)</li> <li>extraction1_pos_posnegtrail_conf09.txt - ds conf 0.9 (partial dataset)</li> </ol> </li> <li>The physio test set is a dataset comprising 153 PICO annotated randomized controlled trial abstracts from Physiotherapy and Rehabilitation. This dataset was used as an additional benchmark to evaluate the generalization power of the weakly annotated dataset and NER model for this sub-domain.</li> </ol> <p> </p> <p><strong>Utility</strong></p> <p>The dataset could be used as an input for training 'Intervention' named-entity recognition (NER) models.</p> <p> </p> <p><strong>Availability</strong></p> <p>This directory includes extraction1_pos_posnegtrail_conf09.txt - This text data file contains all the weak annotations (source intervention terms mapped onto target sentences) from clinicaltrials.org (CTO) with a confidence score of 0.9 and above.</p> <p>The directory also includes ‘physio_sent_annot2POS_posnegtrail.txt’ – This data file contains manually annotated (Intervention entity) data from the physiotherapy and rehabilitation domain. It follows a roughly similar structure as described in the ‘Description for long targets’ section. (‘Participant’ and ‘Outcome’ annotations are removed from this file)</p>
Extracting Biomedical Entities from Noisy Audio Transcripts--Dataset
<p><strong>SUMMARY</strong>:</p> <p>This repo contains the CADEC and Synthetic BTACT datasets that were used for the paper titled "<em>Extracting Biomedical Entities from Noisy Audio Transcripts</em>."</p> <p>The dataset includes two sets: i) CADEC (Karimi et al., 2015) and ii) Synthetic BTACT. CADEC is a well-known NER dataset used to identify adverse drug reactions based on what patients have written about their experiences. Synthetic BTACT is the data that we have made up. It is created based on questions similar to those in the Brief Test of Adult Cognition by Telephone (BTACT)(Tun et al., 2006).</p> <p>CADEC includes two sets of audio files; one is read from the original CADEC, and the other one is with additional audio noise. It also includes the original CADEC scripts, annotations, and the transcripts of the noisy audio. The transcripts are generated using Whisper. The annotations encompass named entities, their types, and string indexes of their occurrence in the text. Annotations also include "AnnotatorNotes" which explains some of the annotations.</p> <p>The synthetic BTACT data include two types: i) animals and ii) fruits. Similar to CADEC, it includes two sets of audio files: one that is read from the original scripts and another one with additional audio noise. The text files include the original scripts, annotations, and the Whisper-transcribed of the noisy audio files. The annotations include indexes of named entities, their string indices and types.</p> <p><strong>REFERENCES</strong>:</p> <p>Karimi, S., Metke-Jimenez, A., Kemp, M., & Wang, C. (2015). Cadec: A corpus of adverse drug event annotations. Journal of biomedical informatics, 55, 73-81.</p> <p>Tun, P. A., & Lachman, M. E. (2006). Telephone assessment of cognitive function in adulthood: the Brief Test of Adult Cognition by Telephone. Age and Ageing, 35(6), 629-632.</p> <p><strong>DETAILS</strong>:</p> <p>Data_1: CADEC (1250 TextFiles, 1000 Audio, types=5):</p> <p> General Categories and Counts<br> ADR (Adverse Drug Reactions): 5316<br> DRUG: 1797<br> FINDING: 397<br> DISEASE: 280<br> SYMPTOM: 255<br> Specific Items (Drugs) and Counts<br> Arthrotec: 145<br> cambia: 4<br> cataflam: 10<br> diclofenac-potassium: 3<br> diclofenac-sodium: 7<br> flector: 1<br> Lipitor: 997<br> Pennsaid: 4<br> solarez: 3<br> voltaren: 46<br> voltaren-rx: 22<br> zipsor: 5</p> <p>Data_2: Synthetic BTACT (500 Fruits, 500 Animals, types=2)</p> <p>>> Audios can be matched with annotations, scripts and transcripts using their filenames. </p> <p>---<br>audio [original&noisy]:<br> 1. cadec<br> 1.1 cadec original<br> 1.2 cadec noisy<br> 2. synthetic btact<br> 2.1 btact original<br> 2.1.1 fruits<br> fruit-script[0:500].mp3<br> 2.1.2 animals<br> script-[0:500].mp3<br> 2.2 btact noisy<br> 2.2.1 fruits<br> fruit-script[0:500].mp3<br> 2.2.2 animals<br> script-[0:500].mp3<br>text[scripts, annotations, transcripts]:<br> 1. cadec<br> 1.1 scripts [1,250]<br> 1.2 annotations [1,250] (index/AnnotatorsNote, type, indices, named-entities)<br> 1.3 transcripts [1,000]<br> 2. synthetic btact<br> 2.1 animals<br> 2.1 scripts (original scripts)<br> script-[0:500].txt<br> 2.2 annotations<br> script-[0:500].ann (index, type, start/end indices, named entity)<br> 2.3 transcripts<br> script-[0:500].txt<br> 2.2. fruits<br> 2.1 scripts (original scripts)<br> script-[0:500].txt<br> 2.2 annotations<br> script-[0:500].ann (index, type, start/end indices, named entity)<br> 2.3 transcripts<br> fruit-script-[0:500].txt</p> <p> </p> <p><strong>CITATION</strong>:</p> <p>Ebadi, N., Morgan, K., Tan, A., Linares, B., Osborn, S., Majors, E., Davis, J., & Rios, A. (2024). Extracting biomedical entities from noisy audio transcripts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).</p>
A rare entity - percutaneous lead extraction in a very late onset pacemaker endocarditis - case report and review of literature
<p><strong>Video 1.</strong> Transthoracic two-dimensional echocardiography apical 4-chamber view: large hypoechogenic hyper-pedunculated mobile mass at the level of tricuspid valve; <strong>Video 2.</strong> Transesophageal two-dimensional echocardiography: low echogenicity pedunculated mass attached to the pacemaker lead, with multiple sites of binding, with no supplementary involvement of the tricuspid valve and myxomatous appearance of the posterior leaflet with hypermobility and rupture of chordae; <strong>Video 3. </strong>Fluoroscopy during the lead extraction procedure, after freeing the lead from adhesions: traction of the lead from the right ventricular apex, through the tricuspid valve, right atrium, superior vena cava and left subclavian vein, to complete lead removal; <strong>Video 4.</strong> Postprocedural transesophageal two-dimensional echocardiography: posterior leaflet chordae rupture, no residual vegetation, no pericardial effusion; <strong>Video 5.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, no additional cardiac masses, posterior leaflet chordae rupture of the tricuspid valve; <strong>Video 6.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, color Doppler: mild tricuspid regurgitation, with two small thin jets.</p>
BiodivNERE: Gold Standard Corpora for Named Entity Recognition and Relation Extraction in Biodiversity Domain
<p>BiodivNER+RE are two gold-standard manually annotated corpora that are meant to be for Named Entity Recognition (NER) and Relation Extraction (RE) tasks based on abstracts and metadata files from the biodiversity domain</p> <p>Such corpora are designed for machine learning techniques, for example, NER as TokenClassification technique, and RE as SequenceClassification technique.</p>
Weekly supervised Multilingual Data Set to train Named Entity Recognition for Symptom Extraction
<p>Data Sets were generated using the Weakly Supervised NER pipeline (https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) to train the symptom extraction NER models. </p> <p><strong>Supported Languages and dataset locations for the specific language:</strong></p> <p> English (base language): https://huggingface.co/HUMADEX/english_medical_ner<br> German: https://huggingface.co/HUMADEX/german_medical_ner<br> Italian: https://huggingface.co/HUMADEX/italian_medical_ner<br> Spanish: https://huggingface.co/HUMADEX/spanish_medical_ner<br> Greek: https://huggingface.co/HUMADEX/german_medical_ner<br> Slovenian: https://huggingface.co/HUMADEX/slovenian_medical_ner<br> Polish: https://huggingface.co/HUMADEX/polish_medical_ner<br> Portuguese: https://huggingface.co/HUMADEX/portugese_medical_ner</p> <p> </p> <p><strong>Dataset Building </strong></p> <ul> <li>Data Integration and Preprocessing</li> <li>Data Cleaning</li> <li>Annotation with Stanza's i2b2 Clinical Model </li> <li>Translation into the targeted language</li> <li>Word Alignment </li> <li>Data Augmentation </li> </ul> <p><strong>Acknowledgement</strong><br>This dataset had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.</p> <p><strong>Authors:</strong><br>dr. Izidor Mlakar, Rigona Sallauka, dr. Umut Arioz, dr. Matej Rojc</p> <p><strong>Please cite as:</strong></p> <p><span>Article title: Weakly-Supervised Multilingual Medical NER For Symptom Extraction For Low-Resource Languages</span><br><span>Doi: 10.20944/preprints202504.1356.v1</span><br><span>Website: </span><a title="https://www.preprints.org/manuscript/202504.1356/v1" href="https://www.preprints.org/manuscript/202504.1356/v1">https://www.preprints.org/manuscript/202504.1356/v1</a></p>
Annotated Dataset for Named Entity Recognition and Relation Extraction in French Building Technical Specifications (BTS)
<p>This dataset contains 233 raw requirements extracted from French Building Technical Specifications (BTS), referred to as "<a href="https://www.aglo.ai/cctp/#:~:text=Le%20CCTP%20(Cahier%20des%20Clauses%20Techniques%20Particuli%C3%A8res)%20est%20un%20document,code%20du%20march%C3%A9%20public%20donc."><em>Cahier des Clauses Techniques Particulières (CCTP)</em></a>", specifically focused on carpentry ("<em>lot menuiserie</em>") in public French construction projects. The requirements have been collected from 72 CCTP documents, resulting in a total of 19,725 sentences and 651,948 words.</p> <p>The dataset has been annotated using <a title="Open-source text annotation tool" href="https://github.com/doccano/doccano">Doccano </a>for Named Entity Recognition (NER) and Relation Extraction (RE). The annotations involve identifying entities and the relationships between them within the domain of building requirements. This dataset is intended for research on Natural Language Processing (NLP) models for Requirements Engineering (RE) in the Architecture, Engineering, and Construction (AEC) sector. Potential applications include requirements extraction, compliance analysis, and knowledge management in construction.</p> <p>The dataset includes the following components:</p> <ol> <li><strong>CCTP Documents</strong>: The original CCTP files from which the raw requirements were extracted.</li> <li><strong>Annotated Dataset</strong>: A JSONLines file containing the annotated dataset, including labels for Named Entity Recognition (NER) and Relation Extraction (RE).</li> </ol> <p>Key features of the dataset:</p> <ul> <li>Language: French</li> <li>Number of requirements: 233</li> <li>Number of sentences: 19,725</li> <li>Number of words: 651,948</li> <li>Annotation tasks: Named Entity Recognition (NER) and Relation Extraction (RE)</li> </ul> <p>This dataset is relevant for NLP research focused on structured information extraction from domain-specific texts in the construction industry.</p>
MEDED: Magi Entity Description Extraction Dataset
<p>Magi Entity Description Extraction Dataset (MEDED) contains 500,000 (entity, description, source URL) tuples extracted by the <a href="https://magi.com/">Magi</a> system from Chinese webpages on the Internet that are accessible from Mainland China in May 2019.</p> <p>These data are learned automatically and should not be considered to contain any opinion of any human individual including the authors.</p> <p>Main contents of the source URLs can be found in the <a href="https://zenodo.org/record/3242512">Magi Practical Web Article Corpus</a>.</p>
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