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Poster: The Tesserae Project: Intertextual Analysis of Latin Poetry

Coffee, Neil, University at Buffalo, SUNY, ncoffee@buffalo.edu

Koenig, J.-P., University at Buffalo, SUNY, jpkoenig@buffalo.edu

Poornim, Shakthi, University at Buffalo, SUNY, poornima@buffalo.edu

Forstall, Christopher, University at Buffalo, SUNY, forstall@buffalo.edu

Ossewaarde, Roelant, University at Buffalo, SUNY, rao3@buffalo.edu

Jacobson, Sarah, University at Buffalo, SUNY,


The Tesserae Project has created a freely available web tool for analyzing text reuse (intertextuality) that automatically identifies matching two-word phrases (bigrams) in Latin poets using one of two search algorithms. Comparison with the results of traditional scholarship demonstrates the efficacy, current limitations, and potential of this approach. Automatic bigram matching by morphological form and dictionary headword detects a significant number of parallels identified by traditional methods. Results so far do not fully replicate traditional scholarship, but the incorporation of further feature sets holds the potential of approaching this standard. Bigram detection produces more systematic results, permits large-scale intertextual study, and identifies less conspicuous parallels.

Computational Approaches to Intertextuality

The reuse of elements from other texts has been understood as a fundamental part of textual signification from ancient Alexandria to modern times. Traditional methods of identifying specific parallels have relied upon the scrutiny and memory of scholars (Hinds 1998, Edmunds 2001). Researchers have recently begun to employ computational means to facilitate and standardize intertextual study, as well as to open new perspectives. Two major lines of approach are phrase (n-gram) matching (e.g. Cummings 2009) and comparison of element length (Holmes 2010). In the field of classical Greek and Latin literature, the Perseus Project has identified five computationally tractable features, including bigram matches, for assessing the similarity of phrases in different texts and has offered a method for cross-linguistic phrase matching (Bamman and Crane 2008, Bamman and Crane forthcoming). A program developed by the eAQUA project locates explicit quotations in the Thesaurus Linguae Graecae corpus of Greek texts (Büchler, Geßner et al. 2010).

Tesserae Search

The goals of the Tesserae Project are to create a website that facilitates intertextual search of classical Latin texts (http://tesserae.caset.buffalo.edu) and to make computational methods and results accessible to traditional scholars. The Tesserae group chose bigram matching as the method most similar to the standard philological search for parallel phrases.

The tool finds similar phrases by matching two words in one text with two words in another. Users can choose two of 26 prepared texts for comparison using one of two search methods. Version 1 matches two identical words from each text, in any order, with no more than four words between them. Version 2 matches words anywhere in an individual sentence by dictionary headword using the Archimedes Project Morphology Service (http://archimedes.mpiwg-berlin.mpg.de/arch/archimedes.new.html), and employs an experimental ranking system to help the assessment of their potential significance. In both versions, the most common words are by default excluded to eliminate potentially insignificant matches. Users can modify search settings with an advanced tab.

Version 1 Test

A Version 1 test compared book 1 of Lucan’s 1st century CE epic Civil War with the whole of the 1st century BCE epic Aeneid by Vergil. The resulting 160 parallels were ranked for significance by traditional philological analysis on a 5 (most significant) to 1 (error) scale. The ranked parallels were compared to those collated from a standard commentary on Lucan’s Civil War (Viansino 1995). Tesserae discovered 87 results judged significant (types 5-3) as compared with 81 of Viansino. Tesserae and Viansino shared only 14 results in common. Tesserae returned results distributed more evenly through Civil War book 1 than Viansino, whose parallels clustered at the beginning and end of the book.

Version 2 Test

Version 2 eliminates errors from Version 1, matches by dictionary headword rather than exact form to account for inflection, and takes whole sentences rather than word windows as the unit of comparison. A Version 2 test again compared Civil War book 1 to the whole Aeneid, and the results were measured against the parallels given by all major Lucan commentators (Heitland and Haskins 1887, Thompson and Bruère 1968, Viansino 1995, Roche 2009). The expanded search parameters of Version 2 returned significantly more results than did Version 1: 2,994 vs. 160. Version 2 produced numbers of types 5 and 4 comparable to Version 1, but considerably more type 3s, as in the following table, where the results of commentators have been collated and graded on the same scale for comparison.

Conclusions

Tesserae fulfills part of its purpose by quickly generating a convenient list of possible intertextual parallels for inspection. The combined tests further demonstrate that Version 1 and Version 2 deliver comparable numbers of the types of parallels scholars have traditionally valued, close morphological similarities of non-frequent words. These results are illustrated in the following chart.

           
Type 5  Type 4  Type 3  Total Significant 
Version 1  19  27  41  87 
Version 2  26  43  262  331 
Viansino 1995  30  17  34  81 
Roche 2009  85  67  170  322 
All commentators*  96  81  195  372 

Parallels between Civil War 1 and Aeneid Found by Tesserae. Versions 1 and 2 Compared with Commentators

       
type 5: strong verbal similarity with meaningfully analogous context 
type 4: moderate verbal similarity with meaningfully analogous context 
type 3: verbal similarity without substantially analogous context 
*All commentators: Heitland and Haskins 1887, Thompson and Bruère 1968, and Viansino 1995, Roche 2009. This counts the total number of unique parallels found, so the same parallel found by different commentators is counted only once. 

Although Tesserae found meaningful parallels, it did not discover the majority of those found by the commentators. Most undetected matches had features that Tesserae does not currently recognize, including similarity of location, meaning, meter, and sound. The results that Tesserae did find, however, appeared in patterns resembling those found by commentators. For example, the commentators found fewer highly significant reference types in the second half of Civil War 1 (type 5, 63 vs. 33 in the second half; type 4, 41 vs. 40), and the combined Tesserae results show a similar, though steeper, decline (type 5, 29 vs. 8; type 4, 30 vs. 27). Tesserae thus supports the cumulative suggestion of the commentators that Lucan establishes substantially more highly significant parallels to the Aeneid in the first half of Civil War book 1 than in the second.

Conversely, Tesserae detects more type 3 references in the first half of the poem than the second, whereas the commentators find the opposite (Tesserae: 158 vs. 122; Commentators: 89 vs. 106). One possible explanation for this difference is that commentators overlook less significant parallels when there are more significant parallels in the same vicinity. On this interpretation, the larger number of significant parallels in the first half of the Civil War led to a correspondingly reduced detection of less significant parallels by commentators, whereas Tesserae detected all types more consistently. Tesserae search thus serves as a complement to and check on traditional analysis in this and other respects, as when in several instances Tesserae returned more links to a given phrase than were noted by commentators.

For longer texts, the proportion of insignificant to significant parallels presented is currently too high for Version 2 to be fully useful, as substantial time is required to sort and analyze the results. Future work will involve developing a system that replicates the manual scoring used for testing to more easily identify different types of parallels. Other planned improvements include the addition of new search parameters and texts, the addition of Greek and other languages, user uploaded texts, and the ability to perform a secondary search for found phrases across a corpus.

References:

Bamman, D. and G. Crane 2008 “The Logic and Discovery of Textual Allusion., ” Proceedings of the Second Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2008), Marrakesh,

Bamman, D. and G. Crane Discovering Multilingual Text Reuse in Literary Texts [forthcoming],

Büchler, M., A. Geßner, et al. 2010 “Unsupervised Detection and Visualization of Textual Reuse on Ancient Greek Texts, ” Proceedings of the Chicago Colloquium on Digital Humanities and Computer Science 1,

Cummings, James 4 September 2009 “TEI-Comparator, ” In my <element/>., (link)

Edmunds, L. 2001 Intertextuality and the Reading of Roman Poetry, Baltimore Johns Hopkins University Press

Heitland, W. E. and C. E. Haskins 1887 M. Annaei Lucani Pharsalia, London G. Bell

Hinds, S. 1998 Allusion and Intertext: Dynamics of Appropriation in Roman Poetry., Cambridge Cambridge University Press

Holmes, M. 2010 “Using the Universal Similarity Metric to Map Correspondences between Witnesses., ” Digital Humanities 2010 Abstracts, (link)

Roche, P., Ed. 2009 Lucan: De bello civili. Book 1, Oxford Oxford University Press

Thompson, L. and R. T. Bruère 1968 “Lucan’s Use of Vergilian Reminiscence, ” Classical Philology, 63 1-21

Trillini, R. H. and S. Quassdorf 2010 “A ‘Key to All Quotations’?: A Corpus-based Parameter Model of Intertextuality., ” Literary and Linguistic Computing, 25 269-86

Viansino, G. 1995 Marco Annaeo Lucano: La Guerra Civile (Farsaglia) libri I-V., Milan Arnoldo Mondadori