This post is part of an ongoing series I am doing about Michael Gavin’s recent article on Empson, statistical semantics, and Milton [1], but it can also be read independently of the others in the series.
Concepts are separable from words
Let’s step away from Gavin’s vector semantics article and take a look at a couple of paragraphs from Gavin’s essay-review of Peter de Bolla’s The Architecture of Concepts: The Historical Formation of Human Rights [2]. Here he separates words from their meanings (p. 250):
For de Bolla, concepts aren’t equivalent to the words people use to denote meanings, nor even the meanings that people might have in mind. Instead, they form the abstract substrate of language, connecting words in ideational structures that enable thought without necessarily rising to the level of consciousness or explicit expression. Concepts are affiliations among words, sometimes teased out logically but just as often left unspoken, so they demand a different kind of analysis. Whereas intellectual histories usually proceed through chronologically arranged close readings, de Bolla’s method attempts to get underneath the (misleading) history of how words were used to direct attention instead to concepts as they really are in an almost Platonic sense: “my aim is to parse the grammar of the concept of rights and to describe its distinctive architecture within the culture of the English language eighteenth century” (63). The “architecture” of a concept is its relationships to other concepts.
Such ideas were common among the computational semanticists of the 1970s and 1980s, thinkers in psychology, computational linguistics, and artificial intelligence (aka cognitive science). Words, more precisely, lexemes, were one thing, concepts another, with much attention being given to the relations of concepts among themselves. These concepts belonged to what came to be called “the cognitive unconscious” as opposed to, for example, the tangle of affect and desire that populations the unconscious of psychoanalysis.
Gavin goes on to assert (p. 252):
Without polemicizing, Architecture of Concepts lays out a theory of conceptuality that has the potential to upend large-scale quantitative research. If concepts exist culturally as lexical networks rather than as expressions contained in individual texts, the whole debate between distant and close reading needs reframing. Conceptual history should be traceable using techniques like lexical mapping and supervised probabilistic topic modeling.
Here I call attention to the word “networks”. Precisely, concepts form networks among themselves, networks that are independent of individual texts. Cognitive scientists turned to the investigation of semantic networks because they were interested in how we understand language or, more pragmatically, in creating computer systems that understand language. That seemed to require a semantics that was separate from the stuff of language–lexemes, syntax, and the rest–and linked to cognition and perception.
As for reframing the debate between distant and close reading, isn’t that what Gavin has done in his article on vector semantics [1]? In this passage he’s talking about a sense of the relationship between words and meaning that emerges from Empson’s critical practice (p. 647):
The playful exuberance of Empson’s method tries to capture something of words’ malleability and extensibility. Meanings aren’t discrete things, even though they inhere to words; instead they unfold over many dimensions of continuously scaled variation. Words have bodies and agency, Empson argues. Even a sort of personhood. They occupy an invisible lexical “thoughtspace” where they break apart and recombine to form superstructures, molding opinions and otherwise forging human experience.
A bit later (p. 659):
Empson always pushed against the dictionary he made use of. Meanings weren’t discrete objects for him but makeshift focal points across which the ambiguities of a poem could be viewed.
I like the assertion that “meanings aren’t discrete things”, something my teacher, David Hays, emphasized to me, referencing the work of his friend and colleague, Sydney Lamb [3] – both are of Chomsky’s generation, but with a very different predilections. Meanings are distributed in a network where the nodes are mutually defining [4]. They aren’t discrete objects.
Gavin finds (more or less) this conception in the vector semantics of Word Spaces. And I rather suspect that his work with vector semantics has influenced his reading of Empson. When he talks of meanings unfolding “many dimensions of continuously scaled variation”, that sounds like vector semantics, not Empson, which is fine.
Models and graphics (a new ontology of the text?)
The investigator working with vector semantics requires all but has to use graphics to visualize phenomena of interest. Lamb used graphics to represent his model, so did Hays. In general, the use of graphics is ubiquitous in this kind of work. The graphics are not incidental or merely visual aids; they are intrinsic to the intellectual style.
This has an interesting and important consequence. Consider a statement from Lamb’s Pathways of the Brain: The Neurocognitive Basis of Language (John Benjamins 1999) where he remarks on importance of visual notation (p. 274): “... it is precisely because we are talking about ordinary language that we need to adopt a notation as different from ordinary language as possible, to keep us from getting lost in confusion between the object of description and the means of description.” That is, we need the visual notation in order to objectify language mechanisms.
Franco Moretti has made similar remarks. This is from page four of the pamphlet, Network Theory, Plot Analysis [5, p. 4]:
Third consequence of this approach: once you make a network of a play, you stop working on the play proper, and work on a model instead: you reduce the text to characters and interactions, abstract them from everything else, and this process of reduction and abstraction makes the model obviously much less than the original object – just think of this: I am discussing Hamlet, and saying nothing about Shakespeare’s words – but also, in another sense, much more than it, because a model allows you to see the underlying structures of a complex object.
By drawing a network of character relationships one has created a model that is clearly distinguishable from the (physical) text itself. One has objectified an (aspect of an) underlying mechanism. What’s important is to have two distinctly different modes of thought available, each used for a distinct intellectual purpose.
Gavin approaches a similar point (p. 666):
This theory of ambiguity implies a new ontology of the text that was intuitively grasped by Empson. Every word in every text is a kind of node connecting that text to a larger universe of language, where the word appears in many variations that supervene over every particular use. Vector semantics embraces his theory’s most radical connotations while lending it greater precision and analytic power. Once a Word Space is modeled, any text becomes an ordered set of points from that space. A text is no longer structured by formal markers of genre, but neither is it reduced to an isolated sequence of words; instead, every word participates in a complex network of relations that collectively make up lexical space as such. The text is reimagined as an ordered subset of that space.
Empson only had words to talk about words and concepts. He couldn’t easily and systematically distinguish between his medium of thought, words, and the texts he was talking about. That, of course, is a problem faced by literary criticism more generally. In contrast, Gavin’s “ordered subset” of a Word Space is a distinctly different conceptual object from the words of text itself.
Are we standing on the shore of a brave new intellectual world?
References
[1] Michael Gavin, Vector Semantics, William Empson, and the Study of Ambiguity, Critical Inquiry 44 (Summer 2018) 641-673: https://www.journals.uchicago.edu/doi/abs/10.1086/698174
Ungated PDF: http://modelingliteraryhistory.org/wp-content/uploads/2018/06/Gavin-2018-Vector-Semantics.pdf
[2] Michael Gavin, Intellectual History and the Computational Turn, The Eighteenth Century, Volume 58, Number 2, Summer 2017, pp. 249-253, DOI: https://doi.org/10.1353/ecy.2017.0019
[3] See, for example, Lamb’s most recent account of his language model, Sydney M. Lamb, Linguistic Structure: A Plausible Theory, Language Under Discussion, 2016, 4(1): 1–37. http://www.ludjournal.org/index.php?journal=LUD&page=article&op=view&path[]=30
Lamb’s discussion is mostly about phonology, morphology, and syntax, though it does touch on semantic, but a relational network pervades the model.
[4] And this seems to be how the nervous system works. See my earlier post, The subjective nature of meaning, New Savanna, July 31, 2018, https://new-savanna.blogspot.com/2018/07/the-subjective-nature-of-meaning.html
[5] Franco Moretti, Network theory, Plot Analysis, Stanford Literary Lab, Pamphlet 2, May 1, 2011. https://litlab.stanford.edu/LiteraryLabPamphlet2.pdf
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