Thanks to a travel grant from the Center for Digital Humanities @ Princeton, I have just completed the intensive week-long Digital Humanities summer school at the OBVIL laboratory at La Sorbonne. OBVIL stands for the “Observatoire de la vie littéraire” or the observatory of literary life. After my Digital Oulipo project and continued work on the Oulipo Archival Project, I cannot agree more with the metaphor of an observatory. Digital Humanities allow researchers to examine from a distance, which complements the traditional literary scholarship of “close readings.” Now more than ever, I believe humanities scholarship needs both perspectives to succeed.
In this intensive and rich program, I was able to continue to develop my skills in XML-TEI that I had been learning through the Oulipo Archival Project. Furthermore, I discovered exciting new software such as TXM, Phoebus, Médite, and Iramuteq and how they can be used to learn more about large corpuses of text. My favorite part of this program was that it was a specifically French introduction to European developments in the digital humanities, allowing me to broaden my perspective on the discipline.
Here is a brief summary of what I learned day by day. I am happy to answer any specific questions by email. Feel free to contact me if you want to know more about the OBVIL summer school, the specific tools discussed there, or just about digital humanities.
The first day of the summer school was a general introduction to the history of digital humanities methods and how to establish a corpus to study using these digital methods. It was especially interesting for me to learn the history of these methods I have been experimenting with for months. I had no idea that the Textual Encoding Initiative (TEI) had been invented in 1987, before I was even born, as a new form of “literate” programming.
Surprisingly, the most useful workshop was a basic introduction to the various states of digital texts. While I knew most of the types of digital documents already as a natural byproduct of using computers in my day-to-day life, it was useful to discuss the specific terminology (in French even!) used to describe these various forms of texts and the advantages and disadvantages of each. For instance, while I knew that some PDFs were searchable while others are not, it was still useful to discuss how to create such documents, the advantages of each, and how to move from one medium to another.
The second day of the summer school began by asking the not-so-simple question of “what’s in a word?” In the following sessions, we learned about everything from how to analyze word frequencies in texts to how to treat natural language automatically, through tokenization (segmenting text into elementary unities), tagging (determining the different characteristics of those unities), and lemmatization (identifying the base form of words). We then had specific workshops meant to introduce us to ready-made tools we could use to treat language automatically. We did not discuss NLTK, however, which I am currently using to program the S+7 method for my Digital Oulipo project, most likely because using NLTK requires a basic understanding of programming in Python, which was out of the scope of this short summer school.
The second half of this day was an introduction to text encoding, how it works and why it is useful for analyzing large corpuses. While I was already familiar with everything covered here, it was still interesting to hear about the applications of TEI to something other than the Oulipo archive. It was especially interesting to hear about applications of TEI to highly structured texts such as 17th century French theater.
This day was extremely technical. First we looked at co-occurrences of characters in Phèdre as an example of network graphs. Since the main technical work had been done for us, it was somewhat frustrating to be confronted with a result that we had no part in creating. While as a former mathematician, I knew how to understand the content of a network graph, many other students did not and took its spatial organization as somehow meaningful or significant. This demonstrates a potential pitfall in digital humanities research. One needs a proper technical understanding of the tools and how they function in order to interpret the results with accuracy.
In addition to network graphs, we also discussed how to use the XPath feature in Oxygen (an XML editor) to count various elements in classical theater such as spoken lines by characters, verses, or scenes in which characters take part. Once again, it was interesting to see how a computer could facilitate such a boring manual labor and how it could potentially be of interest for a scholar, but interpreting such statistical aspects of large corpuses of text is tricky work for someone whose last statistics class was in high school. This gave me the idea to create a course that would properly teach students how to use these tools and understand the results through workshops.
This was another ready-made tool workshop in which we discussed using OBVIL’s programs Médite and Phoebus to edit online texts more efficiently and find differences between different editions. This was very interesting, but probably more useful for publishing houses than for graduate students.
The rest of the day was meant to introduce us to Textometry using TXM, but there were far too many technical issues with the computers provided by the university that we spent the entire time downloading the software on our personal laptops. This was not only frustrating, but ironic. One would think that a summer school in digital humanities run mostly by computer scientists would not have such technical difficulties.
The final day of the program (Friday the 9th was devoted to discussing our personal projects with the staff) continued the work on TXM. In fact, as my section had had such issues the previous day, I decided to switch into the other group. This was a good decision, as the head of that session was more pedagogical in his approach, assigning a series of small exercises to introduce us to TXM. By experimenting with tokenization using TreeTagger and concordance of words, we were able to begin to write a bit of code that could parse a text and find specific groups of words.
This introduction was practical and hands on, but I wish there had been more. While I now know vaguely how to use TXM to analyze texts, I do not have experience coming up with the questions that such techniques might help me answer. This seems to me the key to effective digital humanities scholarship — asking a solvable question and knowing which tools can help you resolve it.