Reclassifying the Encyclopédie

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Diderot and D'Alembert's Encyclopédie might almost have been designed as a document classification exercise. For starters, it comes complete with a branching, hierarchical ontology of classes of knowledge. Out of the 77,000+ articles contained therein, 60,000 are classified according to this system, while 17,000 were left unclassified, providing a ready-made training set and evaluation set, respectively. To make it challenging, within the classified articles, the editors have chosen to apply some classifications with obfuscatory intent or, at least, result, rendering topic boundaries somewhat fuzzy. The categories span the entire breadth of human knowledge, and the articles range from brief renvois ("see XXX") to protracted philosophical treatises. In short, it has everything to make a machine learner happy, and miserable, in one package.

At ARTFL we've been mining this rich vein for some time now. We presented Mining Eighteenth Century Ontologies: Machine Learning and Knowledge Classification in the Encyclopédie at Digital Humanities 2007, detailing our initial attempts at classification and the critical interpretation of machine learning results. We followed up at DH 2008 with Twisted Roads and Hidden Paths, in which we expanded our toolkit to include k-nearest-neighbor vector space classifications, and a meta-classifying decision tree. Where we had previously achieved around 72% accuracy in categorizing articles medium-length and long articles using Naive Bayes alone, using multiple classifiers combined in this way we were able to get similar rates of accuracy over the entire encyclopedia, including the very short articles, which are quite difficult to classify due to their dearth of distinctive content. This post describes an effort to productionize the results of that latter paper, in order to insert our new, machine-generated classifications into our public edition of the Encyclopédie.

For the impatient, jump ahead to the ARTFL Encyclopédie search form and start digging. The new machine generated classifications can be searched just as any other item of Philologic metadata, allowing very sophisticated queries to be constructed.

For instance, we can ask questions like "Are there any articles originally classified under Géographie that are reclassified as Philsophie?" In fact there are several, and it's interesting to peruse them and deduce why their original classifications and generated classifications fall as they do. The editors followed a policy of not including biographies in the Encyclopédie, but evidently could not restrain themselves in many cases. Instead of creating a biography class, however, they categorized such entries under the headword corresponding to the region of the notable person's birth, and assigned it the class Géographie. Thus the article JOPOLI contains a discussion of the philosopher Augustin Nyphus, born there in 1472, and hence is classified by our machine learner under Philosophie.

Our goals in re-classifying the Encyclopédie are several: to provide better access for our users by adding class descriptions to previously unclassified articles; to identify articles that are re-classified differently from their original classes, allowing users to find them by their generated classes which are often more indicative of the overall content of an article; and to identify interesting patterns in the authors' uses of their classification system, again primarily by seeing what classes tend to be re-classified differently.

We initially undertook to examine a wide range of classifiers including Naive Bayesian, SVM and KNN vector space, with a range of parameters for word count normalization and other settings. After examining hundreds of such runs, we found two that, combined, provided the greatest accuracy in correctly re-classifying articles to their previous classifications: Naive Bayes, using simple word counts, and KNN, using 50 neighbors and tf-idf values for the feature vectors.

Each classifier alone was right about 64% of the time -- but together, at least one of them was right 77% of the time. If we could only decide which one to trust when they differed on a given classification decision, we could reap a substantial gain in accuracy on the previously classified articles, and presumably get more useful classifications of the unclassified articles. We must note that the class labels for each article, which appear at the beginning of the text, we retained for these runs, giving our classifiers an unfair advantage in re-classifying the articles that had such labels present. The class labels get no more weight, however, than any other words in the article. We retained them because our primary objective is to accurately classify the unclassified articles, which do not contain these labels, but may well contain words from these labels in other contexts.

It turned out that KNN was most accurate on smaller articles and smaller classes, whereas Naive Bayes worked best on longer articles that belonged to bigger classes, which gave us something to go on when deciding which classifier got to make the call when they were at odds with each other. By feeding the article and class meta-data into a simple decision tree classifier, along with the results of each classifier, we were able to learn some rules for deciding which classifier to prefer for a given decision where they disagreed on the class assignment. See the decision tree in the DH 2008 with DH 2008 paper for the details.

Of course, we couldn't make the perfect decision every time, but we were close enough to increase our accuracy on previously classified articles to 73%, 9% higher than the average of the individual classifiers. By using a meta-classifier to learn the relative strengths and weaknesses of the sub-classifiers, we were able to better exploit them to get more interesting data for our users, and peel back another layer of the great Encyclopédie. Additionally, we learned characteristics of the classifiers themselves that will enable us to target their applications more precisely in the future.

P.S.: For all you Diderot-philes, here are the stats on the original and machine-learned classes of the articles he authored:

If any of this piques your interest, please do get in touch with us (artfl dawt project at gee-male dawt com should work). We'd love to discuss our work and possible future directions. Or come check us out in Virginia next week!
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