I’m still fairly new at this gig, so take the following with a grain of salt. But the more I explore the text-mining side of DH, the more I wonder whether we need to rethink our priorities.
Over the last ten years we’ve been putting a lot of effort into building tools and cyberinfrastructure. And that’s been valuable: projects like MONK and Voyant play a crucial role in teaching people what’s possible. (I learned a lot from them myself.) But when I look around for specific results produced by text-mining, I tend to find that they come in practice from fairly simple, ad-hoc tools, applied to large datasets.
Ben Schmidt’s blog Sapping Attention is a good source of examples. Ben has discovered several patterns that really have the potential to change disciplines. For instance, he’s mapped the distribution of gender in nineteenth-century collections, and assessed the role of generational succession in vocabulary change. To do this, he hasn’t needed natural language processing, or TEI, or even topic modeling. He tends to rely on fairly straightforward kinds of corpus comparison. The leverage he’s getting comes ultimately from his decision to go ahead and build corpora as broad as possible using existing OCR.
I think that’s the direction to go right now. Moreover, before 1923 it doesn’t require any special agreement with publishers. There’s a lot of decent OCR in the public domain, because libraries can now produce cleaner copy than Google used to. Yes, some cleanup is still needed: running headers need to be removed, and the OCR needs to be corrected in period-sensitive ways. But it’s easier than people think to do that reliably. (You get a lot of clues, for instance, from cleaning up a whole collection at once. That way, the frequency of a particular form across the collection can help your corrector decide whether it’s an OCR error or a proper noun.)
In short, I think we should be putting a bit more collective effort into data preparation. Moreover, it seems to me that there’s a discernible sweet spot between vast collections of unreliable OCR and small collections of carefully-groomed TEI. What we need are collections in the 5,000 – 500,000 volume range, cleaned up to at least (say) 95% recall and 99% precision. Precision is more important than recall, because false negatives drop out of many kinds of analysis — as long as they’re randomly distributed (i.e. you can’t just ignore the f/s problem in the 18c). Collections of that kind are going to generate insights that we can’t glimpse as individual readers. They’ll be especially valuable once we enrich the metadata with information about (for instance) genre, gender, and nationality. I’m not confident that we can crowdsource OCR correction (it’s an awful lot of work), but I am confident that we could crowdsource some light enrichment of metadata.
So this is less a manifesto than a plan of action. I don’t think we need a center or a grant for this kind of thing: all we need is a coalition of the willing. I’ve asked HathiTrust for English-language OCR in the 18th and 19th centuries; once I get it, I’ll clean it up and make the cleaned version publicly available (as far as legally possible, which I think is pretty far). Then I’ll invite researchers to crowdsource metadata in some fairly low-tech way, and share the enriched metadata with everyone who participated in the crowdsourcing.
I would eagerly welcome suggestions about the kinds of metadata we ought to be recording (for instance, the genre categories we ought to use). Questions about selection/representativeness are probably better handled by individual researchers; I don’t think it’s possible to define a collective standard on that point, because people have different goals. Instead, I’ll simply take everything I can get, measure OCR quality, and allow people to define their own selection criteria. Researchers who want to produce a specific balance between X and Y can always do that by selecting a subset of the collection, or by combining it with another collection of their own.