Readings for Tuesday (May 7)

• Background Readings in Linguistics Theory
  • Ferdinand de Saussure, Course in General Linguistics (1959)  – read pp. 114-117, 123-27.
  • J. [John] R. [Rupert] Firth, “A Synopsis of Linguistic Theory, 1930-55” (Oxford: Blackwell, 1957) – read sections III-IV (pp. 7-13) [available on course Canvas site]
• Due Tuesday, May 11th: Topic Modeling Exercise

Student proposals

Andrew Goldstone and Ted Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” (2014)

• Andrew Goldstone, Topic model of 100 years of literary criticism journals (visualized in Goldstone’s Dfr-browser interface)

• Andrew Goldstone et al., Topic model of 40 years of the Signs journal of “Women in Culture and Society” (visualized in Goldstone’s Dfr-browser interface)

Andrew Piper, excerpt from “Topoi (Dispersion),” in Enumerations: Data and Literary Study (2019)  — read only pp. 66–75

And yet, despite the growing body of work on topic models, no one has stopped to ask the question “What is a topic?,” either in the classical rhetorical sense or in the computational one. If we have this new way of deriving semantic significance from texts at a large scale, how does it fit within the longer philosophical and philological traditions of understanding “topics”? What, in other words, do these lists of words mean? (67)

… I will begin with an overview of the history of thinking about topics, from Aristotle to Renaissance commonplace books to nineteenth-century encyclopedism. Understanding how topic modeling fits within this longer tradition of deriving coherent categories of thought from a surplus of information — where there has always been a surplus from a single human perspective — will help us see how computation has a distinct pre-computational past. (68)

Commonplace Books

• Wikipedia, “Commonplace Book”
• Trisha M., “Common Quotes in a Commonplace” (2016)
• Kelsey McKinney, “Social media: Nothing new? Commonplace books as predecessor to Pinterest” (2015)
• John Locke, page from one of his commonplace books.
  

David M. Blei, “Probabilistic Topic Models” (2013)

Imagine searching and exploring documents based on the themes that run through them. We might “zoom in” and “zoom out” to find specific or broader themes; we might look at how those themes changed through time or how they are connected to each other. Rather than finding documents through keyword search alone, we might first find the theme that we are interested in, and then examine the documents related to that theme. (77)

[Note]: Indeed calling these models “topic models” is retrospective — the topics that emerge from the inference algorithm are interpretable for almost any collection that is analyzed. The fact that these look like topics has to do with the statistical structure of observed language and how it interacts with the specific probabilistic assumptions of LDA. (78n.)

Ted Underwood, “Topic Modeling Made Just Simple Enough” (2012)

Of course, we can’t directly observe topics; in reality all we have are documents. Topic modeling is a way of extrapolating backward from a collection of documents to infer the discourses (“topics”) that could have generated them. (The notion that documents are produced by discourses rather than authors is alien to common sense, but not alien to literary theory.)

As a literary scholar, I find that I learn more from ambiguous topics than I do from straightforwardly semantic ones. When I run into a topic like “sea,” “ship,” “boat,” “shore,” “vessel,” “water,” I shrug. Yes, some books discuss sea travel more than others do. But I’m more interested in topics like this:

A topic like this one is hard to interpret. But for a literary scholar, that’s a plus. I want this technique to point me toward something I don’t yet understand, and I almost never find that the results are too ambiguous to be useful. The problematic topics are the intuitive ones — the ones that are clearly about war, or seafaring, or trade. I can’t do much with those.

Andrew Piper, excerpt from “Topoi (Dispersion),” in Enumerations: Data and Literary Study (2019)  — read only pp. 66–75

And yet, despite the growing body of work on topic models, no one has stopped to ask the question “What is a topic?,” either in the classical rhetorical sense or in the computational one. If we have this new way of deriving semantic significance from texts at a large scale, how does it fit within the longer philosophical and philological traditions of understanding “topics”? What, in other words, do these lists of words mean? (67)

… I will begin with an overview of the history of thinking about topics, from Aristotle to Renaissance commonplace books to nineteenth-century encyclopedism. Understanding how topic modeling fits within this longer tradition of deriving coherent categories of thought from a surplus of information — where there has always been a surplus from a single human perspective — will help us see how computation has a distinct pre-computational past. (68)

Reading topologically provides a new way of attending to the form of language, this time through an attention to the latent quantities of words. It allows us to envision how figure and concentration serve as an essential foundation of human thought, and that their opposites, dispersion and formlessness, are equally essential for the process of intellectual change….Topological reading makes visible the way topics are neither firmly bounded objects stable through time, the transcendentals of philosophical thought, nor clearly evolving genealogical units, the elements of Begriffsgeschichte [history of ideas, or conceptual history] that move coherently from one form to another across linear time.
Studying topics in this way allows us to see how topics ultimately contain a sense of their own otherness, that, like the computational topics used to model them, each topic contains within itself the potentiality of all other topics in the topical space. (70)

… quotation-based models of the commonplace would be replaced in the eighteenth century by new systems of indexing knowledge at the document level, just as the open-ended lists that accompanied topics would be replaced by new forms of compressed, keyword-driven forms such as the modern encyclopedia, which would emerge in the nineteenth century as a staple of the publishing industry. “Amitie” could still be a topical keyword, but in the Encyclopedia of Diderot and D’Alembert it was no longer composed of a list of quotations, but a condensed definition of the thing itself: “the pure exchange of the spirit is simply called acquaintance; the exchange where the heart is concerned is called friendship”….

In this sense, topic modeling can be seen as a natural extension of the document-level system of indexing that became increasingly popular during and after the eighteenth century (and that had its early modern and medieval precursors). (72-73)

At the same time, the disambiguation between topics in topic models is complemented by a greater degree of ambiguity within topics….
The computational topic, by contrast, incorporates that openness within itself. Rather than group statements under a single keyword or phrase, it organizes a heterogeneity of statements under a complex semantic field. It operates according to the principle of many to many. (74)