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ELMo language model (https://github.com/allenai/bilm-tf) used to produce contextual word embeddings, trained on entire Gigafida 2.0 corpus (https://viri.cjvt.si/gigafida/System/Impressum) for 10 epochs. 1,364,064 most common tokens were provided as vocabulary during the training. The model can also infer OOV words, since the neural network input is on the character level.

This code gets a number of tweets as the input and delivers the semantic graph of relationships between entities of those tweets' text. To this aim first it does a series of text cleanings, and then proceeds with entity extraction and resolutions which come in multiple stages. Finally, the code creates the graph in which nodes represent the entities and the link between them indicates the co-concurrency of those entities in at least one tweet of the input data.

We have developed a literature embedding model to identify significant cardiovascular disease (CVD) risk factors and associated information. Our model that trained using literature data and retrieve CVD risk factors and significant information related to a given query. Our model can be used with CVD prediction on cohort data as feature selection (FS) and dimensionality reduction (DR) tasks. This capsule provides all procedures for literature data collection/pre-processing, literature model training process, CVD risk factor identifications, and FS and DR applications for CVD prediction on cohort data.

Restructured version of Gene Expression Omnibus (GEO) that provides a user friendly interface for curating GEO database

Exploring the use of automated image tagging using the Tribune negative collection from the State Library of NSW.

This paper employs techniques and algorithms from the fields of natural lan-guage processing, graph representation learning and word embeddings to assistproject managers in the task of personnel selection. To do so, our approachinitially represents multiple textual documents as a single graph. Then, it com-putes word embeddings through representation learning on graphs and performsfeature selection. Finally, it builds a classification model that is able to estimatehow qualified a candidate employee is to work on a given task, taking as inputonly the descriptions of the tasks and a list of word embeddings. Our approachdiffers from the existing ones in that it does not require the calculation of keyperformance indicators or any other form of structured data in order to operateproperly. For our experiments, we retrieved data from the Jira issue trackingsystem of the Apache Software Foundation. The evaluation results show, inmost cases, an increase of 0.43% in the accuracy of the proposed classificationmodels when compared against a widely-adopted baseline method, while theirvalidation loss is significantly decreased by 65.54%

The National Museum of Australia provides access to its collection data through an API. As well as collection items, data is available for parties, places, media, and more. The notebooks in this repository give examples of harvesting, analysing, and visualising data from the NMA collection.

Code for checking the status of URL links in the dataset: Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. https://doi.org/10.5255/UKDA-SN-851566 {"references": ["Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-851566"]}

A python module to tokenise texts in the Alsatian dialects. See the module header for help on how to use the tokeniser. The module requires Python 2.7. This tool was developed in the context of the RESTAURE project, funded by the French ANR. The tokeniser is also decribed in the following article: https://hal.archives-ouvertes.fr/hal-01539160. Version 1.4.1 fixes a bug occurring when the space is missing after a comma.