Beowulf Unlocked

New Evidence from Lexomic Analysis

Michael D.C. Drout, Yvette Kisor, Leah Smith, Allison Dennett, Natasha Piirainen

• Publisher: Palgrave Macmillan
• Year: 2016
• ISBN:
  9783319306285
• Topic: Languages & Linguistics
• Subtopic: Linguistics
• Index: Languages & Linguistics

The most original and ground-breaking work on Beowulf in several decades, this book uses "lexomic" methods that blend computer-assisted statistical analysis with traditional approaches to reveal new and surprising information about the construction and sources of the greatest surviving Old English poem. Techniques of cluster analysis identify patterns of vocabulary distribution that indicate robust similarities and differences among segments of the poem. The correlation of these patterns with knowledge gained from source-study, philological analysis, and neglected previous scholarship sheds new light on the material of which Beowulf was made and the way it was composed. The implications of this investigation for the dating, structure, and cultural context of Beowulf will overturn the current scholarly consensus and significantly improve our understanding of the poem, its nature, and origins.

© The Author(s) 2016

Michael D.C. Drout, Yvette Kisor, Leah Smith, Allison Dennett and Natasha PiirainenBeowulf Unlocked10.1007/978-3-319-30628-5_1

Begin Abstract

1. Introduction

Michael D. C. Drout¹ , Yvette Kisor², Leah Smith³, Allison Dennett⁴ and Natasha Piirainen⁵

(1)

Wheaton College, Dedham, Massachusetts, USA

(2)

Ramapo College of New Jersey, Mahwah, New Jersey, USA

(3)

Providence, Rhode Island, USA

(4)

Portsmouth, New Hampshire, USA

(5)

Mechanic Falls, Maine, USA

 

Abstract

New methods of “lexomic” analysis can shed light on even a poem as well-studied as Beowulf.

Keywords

LexomicsLexomic methods Beowulf Digital humanities

End Abstract

In a field as mature as Beowulf studies, the discovery of new data is a rare event. Possessed of a two-hundred-year critical history written by generations of sharp-eyed scholars, the text of Beowulf would seem to have yielded all extractable information, and indeed, the larger recent debates in the field have been over the interpretation of the evidence already accumulated. The possibility of discovering new evidence has not been much entertained, and reasonably so: the chance that a trove of Anglo-Saxon manuscripts relating to Beowulf could come to light is infinitesimal, and even the most remarkable archeological discoveries ¹ are unlikely to be directly applicable to the text of the poem itself. Furthermore, in the past three decades philological and linguistic methods, previously so productive, have failed to uncover many new facts, ² suggesting, perhaps, that their capabilities are exhausted or that Beowulf is simply mined out. But just as advances in technology allow valuable minerals to be recovered from abandoned tailings heaps, innovations in information processing may enable us to extract new data from old texts: in both cases the technology allows us concentrate that which was previously too diffuse to be useful.

In this study we present the results of our use of “lexomic” ³ methods to analyze the text of Beowulf. These approaches, which use computer-assisted statistical analyses to augment traditional philological and literary techniques, have identified patterns of vocabulary distribution, meter and orthography that, when examined in light of our previous knowledge of the poem, shed new light on Beowulf’s structure, ontogeny and evolution. In this first half of a two-part investigation, we introduce methods of cluster analysis that allow us to calculate and represent visually the relative similarity of the vocabulary distributions of segments of a text. Previous research has shown that, in other Anglo-Saxon texts, these patterns of vocabulary distribution are often correlated with sources, structures and histories. Here, we show how the methods can be adapted to the particular problems posed by Beowulf and that, when combined with more traditional approaches to the poem, cluster analyses can identify relationships of similarity and difference between various sections of Beowulf that might otherwise be too subtle or diffuse to be detected by the unaided eye and mind. ⁴

Since the earliest days of Beowulf scholarship researchers have sought to use the poem in support of arguments about other questions (some only tangentially related to Beowulf itself). ⁵ This tendentious approach has regularly led to significant data being misconstrued or misunderstood and to the insights of previous scholars being neglected. ⁶ Even in more recent studies, in which Beowulf is not used for explicitly nationalistic or other overtly partisan political purposes, observation has often been subordinated to thesis, with the predictable result that some data are under- and others over-emphasized, making it difficult for a logical synthesis to evolve. We seek to avoid this problem—at least as long as possible—by describing the tools and methods and then presenting the data acquired by them without trying to embed interpretive or synthetic conclusions in the discussion (these we will leave for the final sections of each part of the investigation). ⁷ The goal of this structure is to allow interested researchers to adopt those methodologies that they find congenial without by so doing being manipulated, rhetorically or logically, into pre-accepting a specific position on later interpretations. ⁸ To avoid having each piece of new evidence become, at the very moment of its production, entangled in the thicket of the major scholarly disputes, parts of this paper operate at a higher level of abstraction than has been traditional in Beowulf studies. We also, for the most part, reserve until the interpretative section of the paper references to the rich scholarship to be found on nearly every line of the poem. Readers should not infer from this arrangement any rejection of the painstaking, erudite analysis of the details of the poem, the accumulation of which is an intellectual monument. We are merely trying to make our own argument as clear, unencumbered and straightforward as possible, emphasizing the findings and the methods that have produced them.

Notes

1. 1.
   The National Endowment for the Humanities helped sponsor this research with three grants: NEH HD-50300-08, “Pattern Recognition through Computational Stylistics: Old English and Beyond” 2008-2009; NEH PR-50112011, “Lexomic Tools and Methods for Textual Analysis: Providing Deep Access to Digitized Texts” 2011-13; and NEH HD-228732-15. National Endowment for the Humanities, Digital Humanities Start-up grant: “Easing Entry and Improving Access to Computer-Assisted Text Analysis for the Humanities,” 2015-17. Any views, findings, conclusions, or recommendations expressed in this article do not necessarily reflect those of the National Endowment for the Humanities.
    
2. 2.
   In great part due to their being perfected over multiple scholarly generations.
    
3. 3.
   Derived by analogy from “genomics,” the term “lexomics” was coined by Betsey Dexter Dyer in 2002 and first appeared in print in Genome Technology 1.27 (2002).
    
4. 4.
   Clustering methods do have a weakness in that they, by necessity, require us to compare reasonably large segments of a text, limiting the sizes of features that we can resolve. Additionally, cluster methods detect relationships that are discrete rather than continuous, thus making the detection and selection of ideal segment boundaries a matter of trial and error. We overcome some of these limitations in the follow-up to this study, Beowulf Unlocked II: The Evidence of Rolling Window Analysis [forthcoming], in which we use rolling window analysis to allow us to resolve features that are both continuous and smaller than those identified by cluster analysis. Rolling window analysis enables us to depict the distribution of metrical, linguistic and orthographic features more accurately, identifying concentrations and allowing us to correlate them with the results of both cluster analysis and more traditional approaches. Combined, the two lexomic techniques compensate for each other’s deficiencies and significantly enhance the knowledge already gained from traditional approaches.
    
5. 5.
   See, among others, Allen J. Frantzen, Desire for Origins: New Language, Old English and Teaching the Tradition. New Brunswick: Rutgers University Press, 1990, 168–200; T. A. Shippey and Andreas Haarder, Beowulf: The Critical Heritage. London: Routledge 1998, 1–74 and passim; T. A. Shippey, The Shadow-Walkers: Jacob Grimm’s Mythology of the Monstrous. Tempe: Arizona Center for Medieval and Renaissance Texts and Studies, 2005; Joep Leerssen, National Thought in Europe: A Cultural History. Amsterdam: Amsterdam University Press, 2006; Andrew Wawn, Graham Johnson and John Walter, Constructing Nations, Reconstructing Myth: Essay in Honour of T. A. Shippey. Turnhout: Brepols, 2007.
    
6. 6.
   Theodore Andersson’s assessment that “the institutional memory in Beowulf studies is about an even century” may even be optimistic. Theodore Andersson, “Sources and Analogues,” in Robert E. Bjork and John D. Niles, eds. A Beowulf Handbook. Lincoln, NE: University of Nebraska Press, 1998, 129.
    
7. 7.
   In so doing we will try to live up to the example of Levin Schücking, who in 1905 showed that it was possible to present evidence both for and against a preferred argument and to be fair-minded when evaluating it and the arguments of others. Levin Schücking, Beowulfs Rückkehr: eine kritische Studie. Studien zur englischen Philologie 21. Halle: Max Niemeyer, 1905.
    
8. 8.
   We are trying to avoid presenting our argument as a “case lawyer’s plea,” instead seeking to set out the evidence “in clear detail for a sober and dispassionate judge”; Johan Gerritsen, “Have with you to Lexington! The Beowulf Manuscript and Beowulf,” in In Other Words, Transcultural Studies in Philology, Translation and Lexicography Presented to Hans Heinrich Meier on the Occasion of his Sixty-Fifth Birthday, ed. J. Mackenzie and R. Todd, Dordrecht: Foris, 1989, 15–34 at 15.
    

© The Author(s) 2016

Michael D.C. Drout, Yvette Kisor, Leah Smith, Allison Dennett and Natasha PiirainenBeowulf U...

Table of contents

• Cover
• Frontmatter
• 1. Introduction
• 2. Lexomic Methods
• 3. Text Preparation of Beowulf
• 4. Cluster Analysis of Beowulf
• 5. Interpretation of the Cluster Analysis
• 6. Conclusions Drawn from Cluster Analysis
• Backmatter