{"id":1715,"date":"2019-06-24T02:39:02","date_gmt":"2019-06-24T02:39:02","guid":{"rendered":"http:\/\/blogs.dickinson.edu\/dcc\/?p=1715"},"modified":"2019-06-24T12:34:09","modified_gmt":"2019-06-24T12:34:09","slug":"concordance-liberation-terence","status":"publish","type":"post","link":"https:\/\/blogs.dickinson.edu\/dcc\/2019\/06\/24\/concordance-liberation-terence\/","title":{"rendered":"Concordance Liberation: Terence"},"content":{"rendered":"
\"Scantily<\/a>

Thalia, Muse of Comedy, from the Goddesses of the Greeks and Romans series (N188) issued by Wm. S. Kimball & Co.,1889. Metropolitan Museum.<\/p><\/div>\n

The plays of Terence<\/a> (P. Terentius Afer) are widely admired for their pure Latin style, but there is as yet no parsed text in digital form that would permit valid statistical analysis of his language and the creation of accurate vocabulary lists to ease reading via tools like The Bridge<\/a>. If and when DCC publishes an edition of a play of Terence, having a text in which each word form is associated with its correct dictionary headword (lemma) will make the creation of the vocabulary lists a relative snap. Computers can’t accurately parse texts on their own, but humans used to do it routinely in the genre of book known as the concordance or index verborum<\/a>. With the help of Dickinson computer scientist Michael Skalak, Bret Mulligan of Haverford and I have been working on project<\/a> to convert older concordances and indices verborum into parsed texts by essentially unscrambling them so they are organized by text location rather then alphabetically by headword, and putting the data into an openly published and freely available spreadsheet. We have successfully completed the transformation of print concordances to Lucretius<\/a>, Apuleius<\/a>, and Eutropius<\/a>, and now we are on to Terence, based on a professionally digitized version of Index Verborum Terentianus<\/em> by Edgar B. Jenkins (Chapel Hill: The University of North Carolina Press, 1932, Pp. ix +187). (Worldcat record<\/a>).<\/p>\n

Jenkins’ book was meticulous, and it was well-received. Writing in Classical Review<\/em> 47.1 (1933) 22-23<\/a> J.D. Craig called it “a miracle of compression without obscurity,” and he spotted only a small number of errors. Jenkins based his index on the text of Knauer and Lindsay, which is still in use (and on PHI<\/a>). In each case, transformation from an alphabetical word list into a sequential parsed text requires careful examination of the system of listing lemmas, word forms, citations, and textual variants. Classical concordances are all slightly different in the conventions they employ.<\/p>\n

The main peculiarity of Jenkins’ books is that he used a system of hyphenation, presumably to save space. This will have to be overcome by alteration of the base code for Michael Skalak’s Concordance Processor (code on Github<\/a>). For my part, I had to filter out some information that was evidently important to Jenkins, but is not to us. For example, Jenkins put in parentheses all citations for words that are in parentheses in the text itself. Whether or not a word is in parentheses is immaterial to us, and having those citations in parentheses would have meant those citations were misinterpreted by the processor.<\/p>\n

For the benefit of anybody who wants to try to do this kind of work in the future (and there are innumerable concordances that could be liberated in this way), here are my working notes and analysis of Jenkins. A random chunk of the .pdf looks like this:<\/p>\n

\"selectionAfter digitization by NewGen Knowledge Works<\/a> it looks like this:<\/p>\n

<il><B>scirp-us:<\/B><\/il>
\n<il> -o (ab): An 941<\/il>
\n<il><B>Scirt-us:<\/B><\/il>
\n<il> -e: Hc 78<\/il>
\n<il><B>sciscit-or:<\/B><\/il>
\n<il> -ari: E 548<\/il>
\n<il><B>scite<\/B> (3): Ht 729 764 785<\/il>
\n<il><B>scit-us<\/B> (pa; 5):<\/il>
\n<il> -a (ns): P 110<\/il>
\n<il> -um (ac): E 254<\/il>
\n<il> -um (n): Ht 210; P 821<\/il>
\n<il> -us: An 486 (in tmesis w per)<\/il>
\n<il><B>scopul-us:<\/B><\/il>
\n<il> -um: P 689(4)<\/il>
\n<il><B>scort-or<\/B> (2):<\/il>
\n<il> -ari: Ad 102; Ht 206<\/il>
\n<il> -atur: Ad 117(F)<\/il>
\n<il><B>scortum<\/B> (ac; 2): Ad 965; E 424<\/il>
\n<il><B>screatus<\/B> (ac): Ht 373<\/il>
\n<il><B>scrib-o<\/B> (19):<\/il>
\n<il> -am (ind): P 127<\/il>
\n<il> -at: P 3<\/il>
\n<il> -endo (g ab): E 7<\/il>
\n<il> -endum (g): Ad 25; An 1<\/il>
\n<il> -ere: Ad 16; E 36; Hc 56<\/il>
\n<il> -eret: Hc 27<\/il>
\n<il> -ito (3): P 668<\/il>
\n<il> -undis (ab): An 5<\/il>
\n<il> -unt: Ht 43<\/il>
\n<il> scripserit (subj): Hc 7a(DT); Ht 7<\/il>
\n<il> scripsit: E 10; Hc 6; Ht 15; P 6<\/il>
\n<il> scripta (sunt): An 283<\/il>
\n<il> scriptam (sc esse): P 329<\/il><\/p>\n

Skalak’s concordance processor will convert this into a spreadsheet with each piece of information in its proper category: lemma or headword (column 1), lemma homonym distinguisher, if any (column 2), citation for specific word forms (column 3), the word forms (column 4), word form homonym distinguishers or other information about a single word form (column 5), and textual variant information (column 6). The trick to the pre-processing analysis is to find the machine-readable characteristics of each kind of information, so the processor can be adjusted to the specific conventions used by the index. Examination revealed the following:<\/p>\n