• Digital Humanities and Cultural Heritage
• 2017-2021close
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A series of Python scripts that have been used to process the documents in the National Edition of Aldo Moro's works. In particular, the scripts are: recast.py, for refactoring the documents code, correcting possible errors, normalizing and finalizing resource URIs, and so on; TEIfy.py, for generating TEI documents from the HTML code. The TEI structure imitates the source code as closely as possible, and in addition integrates bibliographic metadata in the document header as well; PDFfy.py, for generating PDF documents from the HTML code. The PDF pagination and visualization is regulated by a CSS stylesheet (stylesheet.css); generate.py, for converting metadata into semantic statements collected in a knowledge base and organized on the basis of the standards defined in the data modelling phase; align.py, for integrating document markup in the knowledge base; main.py, for gathering and managing the functions defined in the other scripts; utils.py, for choosing which script to run and in which local directory. To start the program, run `python main.py` in the command prompt, then select the function you want to run and the directory you intend to use. You will either need a `config.json` file containing some variables in order to make it work (such as your local paths and names of the directories), or write them directly into the code as variables.

gCube CMS Suite is a distributed full stack application for publication management of complex space-temporal documents called Projects in a gCube based infrastructure (see wiki). The gCube CMS Suite is a gCube Application designed to manage the publication lifecycle of complex space-temporal documents called Projects, supporting their materialisation and Indexing in different platforms (Databases, Catalogues, OGC Services) while maximising reusability. It can manage the entire lifecycle of Projects, from their creation to access including : The application’s main features are Documents Publication Management: CRUD (Create, Read, Update, Delete) operations, publication lifecycle, UCD - based validation; Role-based access to resources and metadata sections; Cooperative and extensible workflows; Automatic Indexing & aggregation based on UCD annotations: GIS indexes (both centroids & multi-polygon), with dynamic aggregation based on zoom level; Text indexes; Extensible Document model: Generic meta-model; Custom extensions of the model; Extensible behaviour Core engine based on a meta-model; Publication management implemented by an extendable set of document Handlers; Workflow configured in UCD by declaring LifeCycle Managers and involved Handlers. Consumption of documents configured in UCD Configurable exploitation of gCube facilities via dedicated Handlers (e.g. SDI Materialisation and Indexing)

The United Nations’ Sustainable Development Goals (SDGs) have become an essential guideline for organizations to monitor and plan their contributions to social, economic, and environmental transformations. The text2sdg package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems and machine-learning-powered ensemble methods, opening up the opportunity to monitor all types of text-based data, such as scientific output or corporate publications.

The purpose of this tool is performing NLP analysis on Telegram chats. Telegram chats can be exported as .json files from the official client, Telegram Desktop (v. 2.9.2.0). The files are parsed, the content is used to populate a message dataframe, which is then anonymized. The software calculates and displays the following information: user count (n of users, new users per day, removed users per day); message count (n and relative frequency of messages, messages per day); autocoded messages (anonymized message dataframe with code weights assigned to each message based on a customizable set of regex rules); prevalence of codes (n and relative frequency); prevalence of lemmas (n and relative frequency); prevalence of lemmas segmented by autocode (n and relative frequency); mean sentiment per day; mean sentiment segmented by autocode. The software outputs: messages_df_anon.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, and the text; usercount_df.csv - user count dataframe; user_activity_df.csv - user activity dataframe; messagecount_df.csv - message count dataframe; messages_df_anon_coded.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, the text, the codes, and the sentiment; autocode_freq_df.csv - general prevalence of codes; lemma_df.csv - lemma frequency; autocode_freq_df_[rule_name].csv - lemma frequency in coded messages, one file per rule; daily_sentiment_df.csv - daily sentiment; sentiment_by_code_df.csv - sentiment segmented by code; messages_anon.txt - anonymized text file generated from the message data frame, for easy import in other software for text mining or qualitative analysis; messages_anon_MaxQDA.txt - anonymized text file generated from the message data frame, formatted specifically for MaxQDA (to track speakers and codes). Dependencies: pandas (1.2.1) json random os re tqdm (4.62.2) datetime (4.3) matplotlib (3.4.3) Spacy (3.1.2) + it_core_news_md wordcloud (1.8.1) Counter feel_it (1.0.3) torch (1.9.0) numpy (1.21.1) transformers (4.3.3) This code is optimized for Italian, however: Lemma analysis is based on spaCy, which provides several other models for other languages ( https://spacy.io/models ) so it can easily be adapted. Sentiment analysis is performed using FEEL-IT: Emotion and Sentiment Classification for the Italian Language (Kudos to Federico Bianchi <f.bianchi@unibocconi.it>; Debora Nozza <debora.nozza@unibocconi.it>; and Dirk Hovy <dirk.hovy@unibocconi.it>). Their work is specific for Italian. To perform sentiment analysis in other languages one could consider nltk.sentiment The code is structured in a Jupyter-lab notebook, heavily commented for future reference. The software comes with a toy dataset comprised of Wikiquotes copy-pasted in a chat created by the research group. Have fun exploring it. {"references": ["Bianchi F, Nozza D, Hovy D. FEEL-IT: Emotion and Sentiment Classification for the Italian Language. In: Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics; 2021. https://github.com/MilaNLProc/feel-it"]}

This is the accompanying code to the Master Thesis research project "Cities for Citizens: Identification of Public Values and their Conflicts in Urban Spaces" conducted in cooperation with the CUSP Lab @ TPM TU Delft and the City Science Lab Hamburg. Aiming to identify public values and their spatial conflicts, a total of nine Jupyter notebooks and one R script follow the research flow as outlined in the Master thesis document.

This code demonstrates generic public examples of a Simple IIIF Discovery system, based on tools used within the National Gallery to provide access to images from multiple institutions and present them together in IIIF compatible viewers. The system is based on a website requesting the details of IIIF images from a Simple IIIF Discovery end-point, based on a simple keyword search. The website does not need to understand the complexities of any underlying APIs, just the simple structure of the results returned by the end-point. The website then just needs to be able to reformat the IIIF results and feed them into a IIIF based viewer of choice. This version includes a number of updates to the original demonstrator related to improving the user interface, including adding a toggle option to jump between IIIF viewers, and updating the administration process of creating new end-points and related web-pages, this is all achieved via a simple JSON config files now. A working demo of this system can be seen at: https://research.ng-london.org.uk/ss-iiif

Marian NMT model for Catalan to Occitan translation. It is a multi-task model, producing also a phonemic transcription of the Catalan source. The model was submitted to WMT'21 Shared Task on Multilingual Low-Resource Translation for Indo-European Languages as a CUNI-Contrastive system for Catalan to Occitan.

Marian multilingual translation model from Catalan into Romanian, Italian and Occitan. Primary CUNI submission for WMT21 Multilingual Low-Resource Translation for Indo-European Languages Shared Task.