1. Linguistic Annotation of Classical Texts
David Bamman
The Perseus Project

2. Classical Philology
“We should not fail to hear the almost benevolent nuances which for a Greek noble, for example, lie in all the words with which he set himself above the lower people—how a constant type of pity, consideration, and forbearance is mixed in there, sweetening the words, to the point where almost all words which refer to the common man finally remain as expressions for "unhappy," "worthy of pity" (compare deilos [cowardly], deilaios [lowly, mean], ponêros [oppressed by toil, wretched], mochthêros [suffering, wretched]—the last two basically designating the common man as a slave worker and beast of burden).”
F. Nietzsche, Genealogy of Morals 1.10

3. Historical treebanks
Most recent research and investment in treebanks has focused on modern languages, but treebanks for historical languages are now arising as well:
Middle English (Kroch and Taylor 2000)
Medieval Portuguese (Rocio et al. 2000)
Classical Chinese (Huang et al. 2002)
Old English (Taylor et al. 2003)
Early Modern English (Kroch et al. 2004)
Latin (Bamman and Crane 2006, Passarotti 2007)
Ugaritic (Zemánek 2007)
New Testament Greek, Latin, Gothic, Armenian, Church Slavonic (Haug and Jøhndal 2008)

4. Design
Latin and Greek are heavily inflected languages with a high degree of variability in its word order: constituents of sentences are often broken up with elements of other constituents, as in ista meam norit gloria canitiem (“that glory will know my old age”).

5. Design
This high level of non-projectivity has encouraged us to base our annotation style on that used by the Prague Dependency Treebank (PDT) for Czech (another non-projective language), while tailoring it for Latin via the grammar of Pinkster (1990). In contrast to the phrase-structure style annotation of other treebanks (e.g., the Penn Treebank), the PDT annotation is based on the dependency grammar of Mel’cuk (1988), which links words to their immediate heads without any intervening non-terminal phrasal categories.

6. Tagset PRED predicate SBJ subject OBJ object ATR attribute ADV
adverbial
ATV/AtvV
complement
PNOM
predicate nominal
OCOMP
object complement
COORD
coordinator
APOS
apposing element
AuxP
preposition
AuxC
conjunction
AuxR
reflexive passive
AuxV
auxiliary verb
AuxX
commas
AuxG
bracketing punctuation
AuxK
terminal punctuation
AuxY
sentence adverbials
AuxZ
emphasizing particles
ExD
ellipsis

7. Latin Dependency Treebank
Author
Words
Caesar
1,488
Cicero
6,229
Sallust
12,311
Vergil
2,613
Jerome
8,382
Ovid
4,789
Petronius
12,474
Propertius
4,857
Total
53,143

8. Ancient Greek Dependency Treebank
Work
Words
Aeschylus (complete)
48,158
Hesiod, Works and Days
6,303
Homer, Iliad
38,390
Homer, Odyssey
99,353
Total
192,204

9. Building Treebanks
Solicit annotations from two independent annotators; reconcile differences between them.
Background: ranges from advanced undergraduates to PhD and professors, with the majority being students in graduate programs in Classics.
Average speed: 124 words per hour. Interannotator accuracy: attachment (ATT), label (LAB), labeled attachment (LABATT):
ATT
LAB
LABATT
Hesiod, W&D
85.1%
85.9%
79.5%
Homer, Iliad
87.1%
83.2%
79.3%
Homer, Odyssey
87.5%
85.7%
80.9%
Total
87.4%
85.3%
80.6%

10. Perseus Digital Library

11. TEI XML
Gallos ab Aquitanis Garumna flumen, a Belgis Matrona et Sequana dividit. Horum omnium fortissimi sunt … (“The Garonneriver separates the Gauls from the Aquitani and the Marne and the Seine (rivers) separate them from the Belgae. The bravest of all of these are …”)

12. Treebank Annotation

13. Treebank Annotation
Graphical editor: build a syntactic annotation by dragging and dropping each word onto its syntactic head.

14. Annotator forum

15. Class treebanking
Currently being used in 8 universities from the United States to Australia.

16. Perseus Digital Library

17. Perseus Digital Library

18. Undergraduate Contributions

19. Undergraduate Contributions

20. Undergraduate Contributions

21. Undergraduate Contributions
Summary:
Class treebanking yields lower overall accuracy than highly trained annotators, but:
Some tasks have high accuracy rates across the board (identify these to maximize undergraduate contributions)
Individual annotators have different skill levels on different kinds of tasks (use to identify strengths/weaknesses in understanding)
The accuracy rates of individual highly trained undergraduates is comparable to that of their more advanced peers.

22. Ownership Model
...

23. Scholarly treebank
Syntactically annotated corpus that reflects an interpretation of a single scholar.
Mambrini Aeschylus.

24. Treebank Data Download it yourself:

25. Use
Treebanks are not just for computer scientists and computational linguists; they provide a large dataset for many different types of inquiry by traditional researchers, including:
rhetoric
historical linguistics
lexicography/philology

26. Rhetoric
Hyperbaton: the transposition of (normally projective) word order for rhetorical effect.
tris notus abreptas in saxa latentia torquet, saxa vocant Itali mediis quae in fluctibus aras (Vergil, Aen )
= tris abreptas notus in saxa torquet, quae saxa in mediis fluctibus latentia Itali aras vocant (Donatus, De Tropis)

28. Rhetoric
Hyperbaton: the transposition of (normally projective) word order for rhetorical effect.
tris notus abreptas in saxa latentia torquet, saxa vocant Itali mediis quae in fluctibus aras (Vergil, Aen )
= tris abreptas notus in saxa torquet, quae saxa in mediis fluctibus latentia Itali aras vocant (Donatus, De Tropis)

29. Rhetoric
Prepositional hyperbaton, where an adjectives occurs to the left of a prepositional phrase that contains its head:
“memorem ... ob iram”
“magna cum laude”
Less frequently, prepositional hyperbaton occurs when the object of the preposition itself occurs outside of the prepositional phrase:
“iram ... ob memorem”

30. Rhetoric Author adj < prep < noun noun < prep < adj Vergil
A treebank lets us locate and measure the rates of this kind of hyperbaton, since it encodes the linear order of words along with their syntactic dependents. Hyperbaton is the interaction of these two variables.
Author
adj < prep < noun
noun < prep < adj
Vergil
40.0%
15.6%
Cicero
8.9% 0%
Caesar
2.2%
Jerome
Prepositional phrase hyperbaton rates. Vergil, n=90; Cicero, n=45; Caesar, n=138; Jerome, n=540.

31. Historical linguistics
Classical Latin word order is generally thought to be SOV (though see Pinkster 1991 for an opposing argument), but eventually transformed into the SVO order found in modern-day romance languages.
Treebanks again provide an excellent resource for answering this question statistically - it can provide a quantitative and reproducible figure that can suggest potentially fruitful directions for further qualitative analysis.

32. Historical linguistics
Cicero
Caesar
Vergil
Jerome
SVO
5.3% 0%
20.8%
68.5%
SOV
26.3%
64.7%
18.8%
4.7%
VSO
6.3%
16.5%
VOS
10.4%
3.1%
OSV
52.6%
35.3%
25.0%
3.9%
OVS
10.5%
Word order rates by author (sentences with overt subjects and objects). Cicero, n=19; Caesar, n=17; Vergil, n=48; Jerome, n=127.

33. Historical linguistics
Cicero
Caesar
Vergil
Jerome OV
68.2%
95.2%
56.2%
13.9% VO
31.8%
4.8%
43.8%
86.1%
Cicero
Caesar
Vergil
Jerome SV
75.9%
86.7%
53.6%
65.8% VS
24.1%
13.3%
46.4%
34.2%
Word order rates by author (sentences with one zero-anaphor). OV/VO: Cicero, n=44; Caesar, n=63; Vergil, n=121; Jerome, n=309. SV/VS: Cicero, n=58; Caesar, n=90; Vergil, n=97; Jerome, n=404.

34. Collaboration Index Thomisticus PROIEL Merge datasets
Share experience/resources
Establish common method of annotation

35. The Index Thomisticus
42,889 words, Scriptum super Sententiis Magistri Petri Lombardi

36. The PROIEL Corpus http://www.hf.uio.no/ifikk/proiel/ Language (Text)
Completed
In Progress
Slavic (Codex Marianus)
4,742
12,804
Latin (Vulgate)
12,414
87,747
Greek (New Testament)
11,439
94,839
Gothic (Bible)
1,654
8,305

37. Collaboration: Comparison
With a single treebank, we can conduct synchronic linguistic or stylistic studies
With multiple treebanks, we can conduct diachronic studies, measuring the evolution of linguistic phenomena over time.
E.g.: Indirect discourse in Latin
ACI vs. Quod clauses

38. Accusativus cum Infinitivo
tu es contentus (“you are content”)
dicebas te esse contentum (“you said that you were content”)

39. Quod/quia clauses

40. ACI -> Quod/quia
Existing studies (Mayen 1889, Cuzzolin 1991/1994, Herman 1989)
Author
Date
Ratio ACI:Quod
Tertullian
c. 235 CE
33:1
Cyprian
c. 258 CE
12:1
Lucifero di Cagliari
c. 370 CE
6:1

41. Methodology
ACI = infinitive verb (or accusative participle with optional “esse”) dependent on head via SBJ or OBJ
Accusative SBJ optional in Latin (pro-drop language)
Inspection still necessary to prune modal and prolative infinitives (dependent on “can” or “begin” etc.)
Quod/quia clause = verbs dependent via argument relation (SBJ/OBJ) on quod/quia, as opposed to adjunct relation of (e.g.) causal clauses (ADV).
Divided all verbs into two classes: verbs of thinking and saying (“you say that you are content”) and impersonal verbs (e.g., “going to the store is permitted”)

42. Results verbs of saying and thinking impersonal verbs Author ACI
Quod/quia
Ratio
Classical authors
182 1
99.5%
Jerome 3 9
25.0%
Aquinas 35 80
30.4%
Author
ACI
Quod/quia
Ratio
Classical authors 33 1
97.1%
Jerome 15
100%
Aquinas 27 72
27.3%

43. Transparency
Kühner himself reported the number of passages he counted: “So hat nach meiner Zählung bei doleo 57 Stellen mit Acc. c. Inf. gegen 4 quod, bei miror 110 gegen 8, bei glorior 19 gegen 2, bei queror 71 gegen 15, bei gaude 84 gegen 9 usw.” (1914:77), although it is difficult to say what he meant by the word “Stelle” and impossible to say which texts his counting is based upon.
P. Cuzzolin (1991)

44. Dynamic Lexicon

45. Inducing selectional preferences
Trained a parser on our Greek and Latin treebanks
Parsed all the texts in our 3.5M word Latin corpus, 4.9M word Greek corpus
To find selectional preferences from this noisy data, we use hypothesis tests (log likelihood etc.) to locate syntactic collocations.

46. Dynamic Lexicon: do (to give)
Latin
English
OLD def.
Log score
Opera
Service (= take pains)
22c
254.2
Obses
Hostage
11a
21.8
Signum
Sign -
12.6
Velum
Sail (= set sail)
18f
7.9
Pecunia
Money (= pay) 6a
7.3
Negotium
Business
6.2
Poena
Penalty 7b
5.6
Possessio
Possession 1c
4.8
Littera
Letter (for delivery)
10a
4.3

47. Dynamic Lexicon: do (to give)
Latin
English
Log score
Obses
Hostage
18.4
Opera
Service
11.9
Suspicio
Suspicion
2.2
Facultas
Faculty
1.8
Signum
Sign
1.5
Latin
English
Log score
Potus
Drink
16.6
Esca
Food
3.3
Requies
Rest
3.0
Glora
Glory
2.4
Terra
Earth
1.6
Strongest OBJ of do in Caesar
Strongest OBJ of do in Jerome
Latin
English
Log score
Osculum
Kiss
8.5
Velum
Sail
5.9
Munus
Gift
3.5
Signum
Sign
2.6
Strongest OBJ of do in Ovid

48. How do you get involved?
Pick a text.
Come tomorrow!