Opera Regeolocation in Venice (1660-1760)

This project is defended by Christophe Bitar and Eliott Bell, master students at the EPFL, in the frame of the course DH-405 Foundations of Digital Humanities, given by Prof. Kaplan, Collège des Humanités, EPFL.

Introduction

[Eliott & Christophe]

GitHub repository (data extraction)

GitHub repository (Website)

Website

Motivation

[Christophe]

Mind map

Research questions

Biography via data

State of the art and litterature

CORAGO + books

Project plan

Here is the plan for our project. The core idea is to represent on an interface the information about operas given in Venise between 1660 and 1760, according to the book of Eleanor Selfridge-Field (Standford 2007). We collect data about composers, writters, dates, opera houses.

After the midterm presentation of the project, we divide the work in two. Christophe works on the interface implementation while Eliott works on the last elements of the database.

The planning is made by modules : if we manage to achieve one part, we can continue and enrich the data. This model guarantees to achieve the Minimal Viable Project, consisting in showing the opera through time and space. The NER extraction, possibly more difficult, would arise only if we have time. We ensure also to have sufficient time to debug the interface and analyze the results.

Planning

Week	Eliott Bell	Christophe Bitar
Week 7	Scan & OCR	Litterature finding
Week 8	Pattern matching	State of the art
Week 9	Midterm presentation
Week 10	Matching and cleaning data	Working on the interface
Week 11	NER of entities	Working on the interface
Week 12	Implement full database - Cleaning data
Week 13	Debugging, feedback, analysis
Week 14	Final report

Methodology

From a book to a database

OCR

The first concrete step of the project was to extract as much relevant information from A History of Venetian Opera and Related Genres as possible in order to create an extensive historical dataset. To that effect, after a digitised copy of the book was found, an optical character recognition (OCR) scan was performed on the PDF file to retrieve the text content. This was done using the python-tesseract library.

Once this was done, some superficial data cleanup was performed in order to remove any non-standard characters that wouldn't be handled correctly by the next few functions.

Pattern matching

Two different methods were used to extract the desired data. For the basic, systematic information about each opera (i.e. its title, writer(s), librettist(s), venue and sorting date), the decision was taken to use pattern matching, as said data was well-structured and lacked context. The procedure was to identify the pattern used by the author of the book, write the regular expression corresponding to that pattern, then match the entries and patterns to retrieve the desired data in an automated procedure.

ADD REGEX ILLUSTRATION

To make data extraction more consistent, a few adjustments had to be made to the regexes in order to make them more flexible, as certain entries contained some inconsistencies and additional information. After the adjustments, the few errors that remained were corrected by hand in the final dataset in order to keep regexes reasonably short.

Once the dataset was consistent, it was exported into a JSON file. In addition to the data, a unique ID was added to each opera entry in order to easily cross-reference the database with the following elements.

NER

In addition to the systematic data on each opera, most entries in the book feature a text paragraph disclosing information about the context the opera was released in. To retrieve some of that information, a named entity recognition (NER) scan was done with the Python spaCy library. For each opera production, a list of named entities, including locations and people, was extracted. The decision was then taken to select the 100 most frequently mentioned entities, along with the entries and specific sentences in which they appear. Some entities were discarded from the set for different reasons, such as lack of interesting information (the most frequent entity was “Venice”), being too vague (e.g. surnames that could refer to multiple people), being inaccurate, etc.

The resulting JSON file features the same UID system as the original database to link them in a consistent manner. Alongside the named entities, the spaCy library allo

Enriching the data

Finding coordinates

[Christophe]

Sources, Wikidata, etc.

Interface design

[Christophe]

Slider, filters, maps, modes, credits, etc. Google AI : trial and errors

Results

Technical Assesment

[Eliott]

What did we/the machine manage to do?

Interface Usability

[Eliott]

Comparison mode

The Comparison Mode, here displaying works by Antonio Vivaldi and Marc'Antonio Ziani. The histogram at the bottom allows us to quickly see that Ziani precedes Vivaldi.

In order to allow comparative data analysis, a "Comparison Mode" was implemented into the interface. It enables users to apply two search filters in parallel.

Historical results

[Christophe]

Composers journeys

Pallarolo

Discussion and limitations

Technical bugs

[Eliott]

• OCR detection:
  • Niccold or Niccole instead of Niccolò (Jommelli) because "ò" isn't present in the English character set used by pytesseract
  • Entry separation done with the "Listed as" line, some entries might have been omitted if that line was scanned wrong
• Pattern matching
• NER:
  • Footnote entities often associated with the wrong entry because of entry separation
  • Context sentences often aren't delimited correctly
• Interface:
  • Bugs with comparison mode
  • Search bar filters the operas on the map, not the theater/composer/librettist lists

Historical limitations of the method

[Christophe]

Domino effect, not sufficient to grasp the daily life

Future possible improvements

[Eliott]

• Linking footnotes with correct entries (document/logical layout analysis?)
• Cleaning data
• Fixing interface bugs
• Adding opera genres for more thorough findings