Computational Discovery and Visualization of the Underlying Semantic Structure of Complicated Historical and Literary Corpora

Computational Discovery and Visualization of the Underlying Semantic Structure of Complicated Historical and Literary Corpora
Walsh, John A., Indiana University, jawalsh@indiana.edu
Hooper, Wally, Indiana University, whooper@indiana.edu
This admirable invention [computation by logarithms] added to the ingenious algorithm of the Indians, by reducing to a few days the labour of several months, doubles—if we may so speak—the life of astronomers, and spares them the errors and disgust inseparable from long calculations . . .

–Pierre Simon de LaPlace (1749-1827) Système du Monde, liv. iv, chap. iv

In addition to offering the possibility of new forms of scholarship beyond the traditional article or monograph, digital humanities and the computational tools developed as part of many digital humanities projects allow scholars to practice an accelerated hermeneutics and analysis on ever larger collections of texts and other cultural artifacts. Most projects that create scholarly digital editions of important corpora are consciously addressing a range of research issues in their respective fields and disciplines. Many of these projects are now also developing components and applying computational tools to exploit their new resources with the goal of solving those problems in an efficient and effective way.

Napier’s logarithms (1614) eased the efforts of early modern astronomers. Today, computational tools promise to accomplish lengthy mechanical tasks of review and notation that generations of scholars have done by hand. Effective methods now exist that will aid trained professionals to see into the heart and structure of digital corpora.

Our project is associated with two major digital editions based at Indiana University: the Chymistry of Isaac Newton Project (http://www.chymistry.org), led by William R. Newman, general editor and co-investigator, which is publishing a digital edition of one hundred nineteen alchemical manuscripts written by Isaac Newton, thirty-two of which are now online; and the Swinburne Project (http://www.swinburneproject.org/), edited by Walsh, which is publishing the works of Victorian poet and critic Algernon Charles Swinburne.

The National Science Foundation has funded a three-year project (2009–12, #0620868) to develop computational tools for the analysis of the alchemical language in Newton alchemical corpus. We are investigating the usefulness of methods from computational linguistics, information retrieval, and network sciences for the analysis of the contents of the Newton corpus and the visualization of its structures. We have been running experiments on IU’s Teragrid supercomputer system and at the Computational Linguistics Lab for the last two years.

We have developed a working suite of analytical tools that support effective real-time investigation of the semantics and structure of the corpus. Data from our analytical tools is fed into another suite of tools, the Network Workbench (http://nwb.slis.indiana.edu/), to produce meaningful visualizations that help the user to comprehend the breadth of the materials and their interconnections.

Newman has already used the tools to demonstrate the order of composition of significant parts of Portsmouth MSS. 3973 and 3975, two of Newton’s most important notebooks recording his alchemical experiments performed at Cambridge.

While the tools are being designed primarily for the analysis of alchemical language in Newton's alchemical corpus, we of course hope the tools will be useful in analyzing documents from other disciplines, and so we are also applying these tools to a significant corpus of literary documents by Swinburne.

Among Swinburne's works are six volumes of collected poems, five volumes of collected tragedies, and a number of volumes of prose literary criticism. The style, vocabulary and structures of Swinburne's 19th-century literary works are obviously quite different from Newton's 17th-century scientific texts. Furthermore, Swinburne's prose criticism is very distinct—in style, vocabulary, and structure—from his poetry. An earlier effort from The Swinburne Project, reported on at last year's conference, focused on thematic networks in Swinburne's 1880 volume Songs of the Springtides. Many instances of networked themes were found by simple keyword searching. More elusive instances were identified by traditional methods of close reading, study, and analysis. We are exploring whether our computational tools are successful at locating some of these more elusive instances of previously identified themes and whether the tools can identify clusters that suggest additional themes and streams of meaning.

Our poster and real-time demonstration of the tools will present our results thus far with the Newton and Swinburne projects.

Methods and Results
We have found that Latent Semantic Analysis (LSA) is very effective for discovering the semantic structure and organization of Newton’s alchemical corpus. LSA methods use singular-value decomposition (SVD) to uncover hidden layers of structure in a body of information and make it possible to capture and expose similarities and dissimilarities across an entire corpus with a single set of operations.

LSA technologies were discussed in the literature in the early 1990s primarily in the context of information retrieval. That was a problem because LSA methods for formulating and executing freeform user queries over growing datasets are less effective than other methods based on Bayesian modeling like Latent Dirichlet Allocation (LDA), Topic Analysis, and Latent Profile Analysis, which superseded LSA after 2003. Many who follow the current literature regard LSA as an old and limited technology. But the goals of close study and analysis are often distinct from the goals of information retrieval, and there is little useful literature on the use of SVD and LSA for actual text analysis beyond a report produced by a team at Sandia National Laboratories in September 2010.

For our purposes, methods like LDA and LPA, which are based on identifying k-dimensional patterns from one set of document to the next and strategically neglecting what is not pertinent to those dimensions, are less useful for text analysis than methods that explicitly capture and model all relations between all words and document chunks in corpora. SVD and LSA do that very well.

Our research plan includes collocational analysis of the Newton manuscripts (almost a millions word of English, Latin, and French), and we are collaborating with IU computational linguist Sandra Kübler to do part-of-speech markup, lemmatization, and sentence parsing. As part of that effort, we have been varying the size of our LSA document chunks downward from 1000 words to 250 and 100, approaching the sentence level where the distinction between “bag-of- words” approaches and syntax-based methods tends to vanish for practical purposes and the two can be welded in combined operations.

We have created web-based components that generate comprehensive lists of correlated results based upon user-selected documents, document fragments, or word. In the case of document-fragment queries, the user can select any pair of correlated fragments and view them side by side. (See Figure 1)

Figure 1: Screen capture of user component showing a side-by-side view of two brief fragments from Portsmouth Add. MS. 3973, f. 19r, and Add. MS. 3975, f.77r: The yellow color and red dots indicate shared vocabulary and passages.

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Strongly correlated fragments (those correlating at 0.7 and above on a scale ranging between -1.0 and 1.0) almost always share long phrases and sentences. The method is robust enough to detect paraphrases too. One SVD calculation detects and maps all the passages in that large, arcane corpus which share vocabulary, phrases, and ideas, saving investigators considerable time in working through those texts and revealing correlations that might otherwise escape notice.

A useful way to present SVD results for interpretation is to graph them as networks—the document chunks or the terms are graphed as nodes and the cosine similarities or correlations between them as edges. Our tools make network graph files for use in the Network Workbench (NWB) tool developed by our colleague Katy Börner’s team at Indiana University. (See Figure 2)

Figure 2: Sample network graph of relations between a small number of correlated fragments in Keynes MS. 41, Dibner MS. 1070A, Yahuda MS. 259, Babson MS. 417, and Royal Society M/M/6/5.

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NWB is designed to be a “large-scale network analysis, modeling and visualization toolkit for biomedical, social science and physics research.” Another of our contributions is testing this tool in humanities research contexts for which it was not initially designed.
Figure 2 shows that two passages on Keynes 41, ff. 12r and 13r, are better correlated with four fragments in other manuscripts than they are with each other (their correlation is 0.46). Newton worked back and forth between sets of reading notes as he attempted to make sense of the alchemical literature. The algorithms and graphs make all the connections visible at a glance.

We expect that the combination of LSA-based tools and network graphs derived from them will provide invaluable means of problem solving and discovery for the experienced researcher who is working with large, complicated corpora.

References:
Börner, K. 2007 “Making Sense of Mankind's Scholarly Knowledge and Expertise: Collecting, Interlinking, and Organizing What We Know and Different Approaches to Mapping (Network) Science, ” Environment and Planning B: Planning and Design, 34 5 808-825 Pion

Deerwester, S. C. Dumais, S. T. Landauer, T.K. Furnas, G. W. Harshman, R. A 1990 ““Indexing by latent semantic analysis.”, ” Journal of the American Society for Information Science, 41 6 391–407

Dunlavy, D. Shead, T.M. Crossno, P.J. Stanton, E.T. September 2010 ““ParaText - Scalable Solutions for Processing and Searching Very Large Document Collections: Final LDRD Report.”, ” “Technical Report SAND2010-6269, ” Sandia National Laboratories Albuquerque, NM and Livermore, CA

Skillicorn, D. 2007 Understanding Complex Datasets. Data Mining with Matrix Decompositions, Boca Raton, FL Chapman and Hall

Walsh, J. 2010 “"Quivering web of living thought:" Conceptual networks in Swinburne's Songs of the Springtides., ” Y. Levin A. C. Swinburne and the singing word: New perspectives on the mature work, Farnham, England Ashgate 29-53