That, I suppose, is the rubric under which I wrote a recent series of posts about Mathew Gavin’s article on Empson, ambiguity, vector semantics, and Milton [1]. That’s a topic that’s been on my mind in a somewhat different form for decades: Why literary critics need to know about computational semantics. Computational semantics was very important to me early in my career; it changed the way I thought about language, literature, and the mind.
For awhile I even thought it would change the conduct of literary criticism. I’d used it to analyze the underlying semantics of a Shakespeare sonnet. As things turned out, however, that was the last literary work I analyzed using that kind of model. It was too complicated and strenuous and subsequent developments did little to change that.
And yet, as I said, it had changed my intellectual life? But how could I convince literary critics of the importance of an analytic tool that I only used on one text? That seems a rather hard sell.
But practical criticism isn’t the only game in town, and computational critics aren’t traditional literary critics. There’s another reason for learning something about constitutive computational semantics (notice that I’ve added ‘constitutive’ back to the phrase): You need to know the lay of the land.
For the last several years it’s looked like close reading vs. distant reading, meaning vs., versus what exactly? But that’s NOT how it is; that’s not the territory. I wrote all those pieces around and about Gavin’s article in part to better map out the territory.
This post clarifies that map. I reprise four contrasts:
close reading <> distant readingmeaning <> semanticsstatistical semantics <> computational semanticscorpus as tool <> corpus as object
Call them the lay of the land.
Difference
Meaning and understanding arise in part through difference and contrast. That’s why we’ve been having this conversation about close reading vs. distant reading, to better understand both terms of the opposition through comparison and contrast. This opposition is often discussed in terms of scale, which I find uninteresting, and meaning, as though distant reading has nothing corresponding to meaning. But of course that’s not true. As Moretti, among others, has pointed out, it has models [2], giving us meaning vs. models.
In the course of my Gavin posts I introduced another contrast, two of them in fact [3]:
meaning vs. semanticsstatistical semantics vs. computational semantics
Meaning is inherently subjective [4]. We analyze meaning through a process of interpretation. Semantics, as I am using the term, is different. Semantic analysis requires an explicit model of the language system. The model is in the domain of objects, it is ontologically objective in Searle’s usage [5]. The whole world of so-called distant reading is ontologically objective in that sense (which doesn’t imply that it is objectively true, for the determination of truth is a matter of epistemology).
THAT is the distinction behind the opposition of close and distant reading. On the one hand we have a world where one interprets the meaning of texts. On the other hand we have a world where one builds semantic models of various kinds. The models used by computational critics are statistical in character. But we also have models that are constitutive in character, hence the distinction between statistical semantics and constitutive computational semantics.
Here the use of ‘computation’ is tricky. The various statistical techniques used in distant reading require a great deal of computing power, but the computer isn’t used to model or simulate a linguistic processes. That’s quite different from the computational semantics I learned early in my career. In that kind of semantics language processes were conceived as computational processes. Computation is thus constitutive of semantics, hence the rather awkward phrase, “constitutive computational semantics”. Moreover this kind of computational semantics shares one of its originating thought streams with the vector semantics Gavin used, machine translation (MT) of natural language. That is, it goes with the territory.
Corpus as tool vs. corpus as object
This brings us to Monday’s post [6] where I contrasted the role of the corpus in topic analysis and in MT. The contrast is simple and obvious in retrospect, but it took a bit of work to get there. In topic analysis the corpus itself is the object of investigation whereas in machine translation it is not. Rather, the corpus is used to build a translation system. The same, by the way, is true of vector semantics as Gavin has used. He wasn’t analyzing a corpus, rather he used a corpus to build a Word-Space he could use to analyze a passage from Paradise Lost.
This gives us another distinction: corpus as tool builder vs. corpus as object of investigation.
What does this have to do with computational semantics of the constitutive kind? In that post I quoted extensively from Martin Kay, a first generation computational linguist. He argued that, in the context of MT, statistical methods constitute what he called an “ignorance model”. If we had a rich and robust constitutive semantics we would need the statistical methods. Hence “statistics are standing in for a vast number of things for which we have no computer model” [7].
That’s simply not the case for topic modeling. Nor is it the case for Gavin’s vector semantics. That, I feel, requires a bit of commentary. But not here and now.
Later.
Reprise: The lay of the land in four contrasts
close reading <> distant readingmeaning <> semanticsstatistical semantics <> computational semanticscorpus as tool <> corpus as object
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] Franco Moretti, Network theory, Plot Analysis, Stanford Literary Lab, Pamphlet 2, May 1, 2011. https://litlab.stanford.edu/LiteraryLabPamphlet2.pdf
[3] William Benzon, Gavin 5: Three modes of literary investigation and two binary distinctions, blog post, August 16, 2018, http://new-savanna.blogspot.com/2018/08/gavin-5-three-modes-of-literary.html
[4] William Benzon, The subjective nature of meaning, blog post, July 31, 2018, https://new-savanna.blogspot.com/2018/07/the-subjective-nature-of-meaning.html
[5] John Searle, The Construction of Social Reality, Penguin Books, 1995.
[6] William Benzon, Computational linguistics & NLP: What’s in a corpus? – MT vs. topic analysis, blog post, July 31, 2018, https://new-savanna.blogspot.com/2018/09/computational-linguistics-nlp-whats-in.html
[7] [1] Kay, M.: A Life of Language. Computational Linguistics 31(4), 425-438 (2005). http://web.stanford.edu/~mjkay/LifeOfLanguage.pdf
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