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The novel is about a man who dreams about his flight through a happy life, but from the start he encounters the first punch of destiny — the hump, his physical disability, which separates him from the community. Still, he believes in a bright and wide world until a second punch of destiny: a hump in the heart, as the narrator says, a psychological one. He falls in love with a girl, but she runs away with a man who is uninterested in spiritual values, but holds possession of some stolen money.
In response, the Hunchback breaks down, retreats from Christianity and from community to a forest and starts his solitary inner life as a misogynist and a kind of a pagan hermit. The novel has no happy end: the narrator just leaves the broken man, who actually tells the story, alone. He is possibly the first character in Lithuanian literature who discovers philosophy as the way out of a miserable life, in which he had lost everything and been left alone.
And to the present day the Hunchback has no companion in Lithuanian literature in terms of his reaction to reality. This final choice made by the Hunchback could be seen as a vision from the periphery of European culture about how to move a bit closer to the cultural centre. The mind-events in Lithuanian literature before The Hunchback had been represented as external processes: feelings that were the cause of sweating, blushing, and weakness in the knees. But the way the main character thinks is rather bookish.
The novel has an interesting publication history. Until , the printing of books in the native language was forbidden in Lithuania by Russia, which had annexed the country from until Almost all Lithuanian-language books during that time were published in Germany or the USA, where strong expatriate communities had settled. Even after the publication ban was lifted in Lithuania, the first edition of the novel The Hunchback was published in New York in , while the author was studying philosophy of art in Moscow.
The first edition of the novel to be published in Lithuania appeared in Plans to translate the novel are currently underway, with the first English version of the novel due for publication at the end of His research interests are in the areas of avant-garde culture, nonlinear narrative, quantitative fiction analysis, literary text generation, and word and image relations. He is a member of WG2 and WG3.
DHQ: Digital Humanities Quarterly: A Genealogy of Distant Reading
The project can be considered a spin-off of this COST action and, as an academic airbridge between Antwerp and Krakow, it will intensify the already strong ties that exist between various research teams in their respective institutions. Although this technology is nowadays also emerging in Humanities research, it is surprising how relatively few applications have been reported so far in the domain of authorship attribution. The few research examples that have been published in this domain focus on micro-blogging data and is hard to extrapolate to longer documents.
The researchers propose a three-year collaboration aimed at the introduction and adaptation of deep learning methods to computational stylistics, with an emphasis on author identification. Our workshop group was very international and brought people from various disciplines together. That was why the step-by-step introductions to different technology-supported methods of topic modelling, stylometry, and data visualisation were a perfect fit.
We were introduced to downloadable software with elaborate graphic user interfaces e. TXM and Gephi as well as portable software Dariah Topics Explorer in development and a stylometry tool based on R-libraries, which required working in the command line. The corpora chosen by the workshop facilitators were mainly selections of British fiction, the North-American Brown Corpus and some smaller fiction corpora in other European languages French, Italian, Hungarian, Slovene.
For me as a historian specialising in visual cultures and politics of the early modern period, these were uncommon sources, but at the end of most workshop sessions, I had some time to apply each method and tool to my own corpora e. The workshop facilitators made sure that all participants were able to keep step and competently answered our questions. In this way, the instructors, too, could have received more in-depth feedback, especially in those cases where their tools were still being updated and improved. Nonetheless, the overall timing of the workshop suited me very well as we had the opportunity to connect with other participants during coffee breaks, lunch, and in the evenings.
It was interesting to hear how other scholars at a similar career level were going to use topic modelling, stylometry, or network analysis in their projects, and I learned a lot about the institutional frameworks and digital cultures in other universities. Finally, the vivid keynote lecture delivered by Prof. Our mutual friend, Simon Mahoney University College London , who knew I was keen to set up collaborations with digital humanists in China, put us in touch and told me that I simply had to speak to Tao.
In November this resulted in a first visit to Nanjing University. I came back blown away by the quality and the level of commitment to Digital Humanities at Nanjing University. I returned home feeling invigorated and full of ideas and plans to deepen this promising collaboration. Moretti is not a satirist. The first pamphlet asks whether computers can recognize literary genres, and the second uses network theory to re-envision plots.
A Genealogy of Distant Reading
As its name suggests, the Lit Lab tackles literary problems by scientific means: hypothesis-testing, computational modeling, quantitative analysis. Moretti would say: So what? As many as 60, other novels were published in 19th-century England — to mention nothing of other times and places. To understand literature, Moretti argues, we must stop reading books. The Lit Lab seeks to put this controversial theory into practice or, more aptly, this practice into practice, since distant reading is less a theory than a method.
In its January pamphlet, for instance, the team fed 30 novels identified by genre into two computer programs, which were then asked to recognize the genre of six additional works. Both programs succeeded — one using grammatical and semantic signals, the other using word frequency.
It turns out, though, that people and computers identify genres via very different features. What happens to Hamlet if you remove Horatio? Right: Hamlet. The most interesting thing about a project is rarely the first thing we think it will be.
Our reading experience, our interpretations of a text, the way it makes us feel: these are the result of may things, but language plays a role in constructing all of them. Words form the basis for everything we get out of reading, so we can work backwards from word to concept. Think about what underlying concepts might be taking shape as a result of particular words. For example, if four of the top five words in a text are male names or male pronouns, that might say something about gender representation in the text.
Personal pronouns might say something about what it means to be a self in your text. Four times more exclamation points than periods? That might say something about the rhetorical impression the author wants to convey. If you have a corpus where the dates for each text are known, you can begin to draw inferences based around language use over time. The trends you see can offer good opportunities to reflect on your own understanding of what happens historically over the same time period.
Alternatively, since we experience individual texts over time, we can examine how the use of a concept or word changes from the beginning of a text to the end. All of this might offer a way into thinking about the text as a whole. It is easy to think that the results the computer gives you are correct, and to take them at their word. After all, how could numbers lie?
- another step forward.
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The truth is, however, that any data is the result of the biases of the people who produced them. Seemingly good statistics can make anything seem like objective truth when there might not be anything more than a pretty picture:.
DISTANT READING VS. CLOSE READING
Your own results might be the result of some setting that you have configured just slightly incorrectly. Or maybe you uploaded the wrong text.
Or maybe you are misunderstanding how the tool works in the first place. If something has you scratching your head, take a step back and see if there is something wrong with your setup. Knowledge about both of these fields can go a long way and give you more meaningful and interesting things to say, but these tools, methods, and ideas should not be beyond anyone. Take a tool out for a spin and see what happens.