Text Visualisation

Understanding Texts Through Visualisation

Michael Barlow [1]

Rice University

 

Proposal

Visualisation techniques, from the use of tables and graphs to more sophisticated three-dimensional abstract representations, are common in the scientific and engineering disciplines. Within the humanities, there is a tradition of research on images evoked by texts and images associated with texts, but not on visualisation of texts themselves. I see no fundamental reason why specialised visualisation tools should not be developed for use on data (i.e., raw or annotated texts) in the humanities and related disciplines in order to advance the study of texts and cultures. I wish to explore what is meant by, and what can be gained through, the use of techniques of text visualisation. As I illustrate below, visualisation has the potential to support interpretative scholarship by pinpointing particular hot spots within a large data pool and by providing visual 'maps' of a text which highlight particular elements or relationships of interest. I expect the outcome of my fellowship to be a review paper (and material for a book). In addition, I will address the role of visualisation of texts in the humanities by creating software tools that provide different 'views' of a text. During the semester of the fellowship, these tools will be experimental in nature, but following on from my research I will create a visualisation package for use by scholars who work on text interpretation.

 

Visualisation is a broad topic and in the following few pages I will try to illustrate and define the areas I wish to focus on by giving examples and discussing the aspects of text visualisation that I will explore. There have been individual visualisation projects in the humanities, notably Bradley and Rockwell [2] (Hume’s Dialogues); Rohrer, Ebert and Sibert (The Shape of Shakespeare); and Unsworth, Parker and Ancona (Dante’s Inferno), but what I wish to investigate is the appropriate form and function of a general visualisation tool that can be applied to any text. [3]

 

Humanities scholars working on concordances and similar analyses have long been concerned with the best way of generating analysis data based on texts[4]. These scholars and others, myself included, have found it informative to, in effect, rip texts apart digitally—to put them in a centrifuge and see which text patterns are rare (e.g., words occurring only once, hapax legomena) and which are frequent. The results of this “restructuring” of the text (the wordlists and concordances) are clearly useful and have been the basis of innumerable scholarly works on a variety of aspects of language and culture, and yet, at the same time, there is a desire not only for the sorted 'mash' in the centrifuge, but also for the original story, for the text as a whole. Luckily, since we are working within a digital universe, we can potentially have both at the same time, namely, the results of restructuring as well as the whole text. From a technical viewpoint we can have both, but we still need to work out how to relate these two views in a way that is useful to a wide range of scholars working with texts in the humanities.

 

Many concordance programs do, of course, provide both views, but they do so in a limited sense. For example, I can use my concordance program, MonoConc (Barlow 2000)[5] to find (and sort in various ways) all instances of the word pitee (pity) in the Canterbury Tales, as shown in the screen shot below. (In actual use, many more concordance lines would be displayed.)

Figure 1: Concordance results and context window

The concordance lines are connected to the text to the extent that if I click on a particular instance of pitee, line 479 above, then the word is highlighted within a fairly large section (15K) of the original text, part of which shows in the upper context window. In the screen shot above, only three lines of this context is visible, but the user could scroll through many lines of the surrounding context, and with some programs the user can view the complete contents of the original file. Using this same program, I can also examine the distribution of the word pitee throughout the Canterbury Tales, as shown below.

Figure 2: Distribution of instances of pitee in Canterbury Tales

In this chart the y-axis represents the whole of the text divided into segments, each segment comprising, in this example, 10% of the text. For each of these sequential segments, the number of finds or hits (of pitee) is displayed, allowing us to see easily that there is only one instance of pitee in the fourth segment, but eight instances in the sixth segment. In this example, the distribution tracks a single word, but it is possible to search for several words and plot their combined distribution in order to get a sense of the use of "themes" within a text.

 

From the two screen shots above, we can see that the program presents three views of the text: (i) the concordance lines; (ii) the (large) context for each concordance line, available on demand; and (iii) the chart showing the distribution of the instances through the text. Since I designed this software, I obviously think that this is a useful way of working with a text, but there are alternatives, given current technology, which I would like to explore during a fellowship semester. I do not plan to add a visualisation component to MonoConc; rather I aim to explore completely new approaches and new tools that apply visualisation techniques to the study of texts.

One question to consider is the following: Is it possible to adopt alternative visualisations that combine both search results and the text in a way that goes beyond the provision of a context of variable size for individual results? One problem with the representation above is that although we can search for multiple items, the relation of individual items to the text as a whole can no longer be discerned. It would be beneficial to be able to search for several distinct items simultaneously and see how they are distributed throughout the text and in relation to one another.

 

Let me give a couple of examples of the sort of approach I might adopt. I have produced a mock-up of a search for two-word alliterations and three-word alliterations in the Canterbury Tales. The two searches, both represented simultaneously in the illustration below, are distinguished by colour. The user can switch between a reduced view of the text (shown here in the background) and a close-up view, which shows the search results in the context of the text itself. In this example, the two search items are phrases, the two and three word alliterative sequences, but more generally the search items may be either two or more words/phrases, or else a mixture of words/phrases and textual features such as division into chapters indicated by mark-up.

Figure 3: Text and detail views of alliteration in Chaucer

 

A basic concordancer such as MonoConc responds to word/phrase queries entered by the user. Essentially, the user can only issue one type of command: show me all strings of characters of the pattern X (where X is the word pitee, for example). This is the engine that drives the program and all the display frills are built on this procedure. I would like to create a program that is built on a fundamentally different command: not “show me all strings X,” but rather transform yourself to show Y (where Y is the set of two or three word alliterations, for example). Another example would be a command to show all the hapax legomena in the Canterbury Tales. The result for The Yeoman’s Portrait would then look like the following.

Figure 4: View of text (Yeoman's Portrait) and results (hapax legomena)

 

This example works quite well; we have the results and the text combined in one view. (I assume that separate views may also be appropriate for some purposes.)

 

More complex transformations of the text may be useful in locating patterns or in identifying areas of interest to the scholar. Below, in another mock-up, I have used colour to indicate the part-of-speech of words in a part of CSC’s call for proposals.

 

Key: noun adjective verb adverb

The CSC Mission: The Center exists to promote the study of cultures across time and around the world, both as unique examples of human behavior and creativity and as interconnected phenomena, which can illuminate one another.
The goal of the Center is to provide a forum for the comparative and interdisciplinary conversations that make visible the connections among cultures and the particularities that divide them. The Center seeks to advance humanistic knowledge both by supporting research that deepens our understanding of particular cultures, and by encouraging the exploration of new configurations of materials, methods, theories, or cultures through interdisciplinary and comparative collaborations.

 

Figure 5: Text and part-of-speech information

A further transformation provides a rather different view where the individual letters are indistinguishable, but the part-of-speech coding can be seen. This view reveals the preponderance of adjectives and nouns in this text, which is indicative of the high information load of academic discourse. In contrast, we see the representation of a spoken dialogue among academics, which contains fewer complex nouns and many more verbs and adverbs.

 

Key: noun adjective verb adverb


Written passage:

●●● ●●● ●●●●●●● ●●● ●●●●●● ●●●●●● ●● ●●●●●●● ●●● ●●●●● ●● ●●●●●●●● ●●●●●● ●●●● ●●● ●●●●●● ●●● ●●●●● ●●●● ●● ●●●●●● ●●●●●●●● ●● ●●●●● ●●●●●●●● ●●● ●●●●●●●●●● ●●● ●● ●●●●●●●●●●●●●● ●●●●●●●●● ●●●●● ●●● ●●●●●●●●●● ●●● ●●●●●●●
●●● ●●●● ●● ●●● ●●●●●● ●● ●● ●●●●●●●●●●●● ●●● ●●● ●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●● ●●●● ●●●● ●●●●●●● ●●● ●●●●●●●●●●● ●●●●● ●●●●●●●● ●●● ●●● ●●●●●●●●●●●●●●● ●●●● ●●●●●● ●●●●
●●●
●●●●●● ●●●●● ●● ●●●●●●● ●●●●●●●●●● ●●●●●●●●● ●●●● ●● ●●●●●●●●●● ●●●●●●●● ●●●● ●●●●●●● ●●● ●●●●●●●●●●●●● ●● ●●●●●●●●●● ●●●●●●●●●●● ●● ●●●●●●●●●●● ●●● ●●●●●●●●●●● ●● ●●● ●●●●●●●●●●●●●● ●● ●●●●●●●●● ●●●●●●● ●●●●●●●● ●● ●●●●●●●● ●●●●●●● ●●●●●●●●●●●●●●●●● ●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●

 

Spoken dialogue:

a: ●●●●● ●●●● ●● ●●● ●●●●●●●● ●●● ●●●●● ●● ●●●●● ●●●●
●●●●●●●●●● ●● ●●● ●●●● ●●●●●●●●● ●●●● ●● ●●●●●
●●●●●●
●●●● ●●●●●●● ●●●●●●●●● ●● ●●● ●●●●● ●●●●●

●●●●
b: ●●●●
a: ●●●
b: ●● ●● '●● ●●● ●●●●●● ●●●● ●●●●● ●●● ●●●● –
a: ●● ●●● ●●●●● ●●●●●● ●●● ●●●●●●●●● ●● ●●● ●●●● ●●●●

●●●
b: ●●●● ●●●● ●●● ●● ●●●●●●●●

 

 

Figure 6: Letter-less text and part-of-speech information

This example can be seen as a combination of the text merged with information from selected mark-up, in this case, part-of-speech tags, which I hope is informative for some and suggestive as a methodology for others.

 

Designing graphical views of texts such as these leads naturally to a discussion of the appropriateness and usefulness of different ways of graphically representing textual information. The purpose of visualisation according to Card, Mackinlay and Shneiderman (1999:6) is 'to aid analysis and facilitate discovery, decision-making, and explanation.' For the CSC project I aim to explore what insights can be gained by providing visualisations of a text either to rapidly locate areas of interest within the text, which will then perhaps be analysed in traditional ways, or to provide views of a text permitting new conceptualisations which would be difficult to arrive at by other means.

 

I am confident about the first of these two uses of visualisation. The human visual system is designed in a way that makes us far better at scanning a visual representation than a text. The more data points there are, the greater the need for a visual representation. Making good visuals (for these purposes) means constructing them in a way that complements our perceptual system and avoids extraneous features that carry no informational load. It is also important to maximise the congruence between the visual data and the “underlying” non-visual representation. For instance, in the diagram above I may have distorted the data by choosing to assign one ● for each letter, rather than for each word. Thus ●●●●●●●● is not more of a noun than ●●●, but the visual representation gives the impression to the viewer that it is—because the length of words is encoded as well as the part-of-speech information. On the other hand, ●●●●●●●● is a “heavier” noun than ●●●, and so if we wish to convey something about the density of a text then the part-of-speech/word length combination may be appropriate.

 

I would obviously be pleased to create examples in which visualisation facilitates all aspects of the analysis of texts. For this, I see no other way forward than to work with different people in the humanities to see if I can provide suggestive, and ultimately insightful, visualisations of texts they regularly work with.

 

Today, graphical representations are widely used in general and specialised media to render in a very useful and natural way certain kinds of information—especially changes in a variable over time. Given the naturalness of visual information, it comes as a surprise to find just how recent graphical representations of any kind are. It was not until the late eighteenth century that time-series plots (graphs with a time dimension on the x-axis) began to be used in scientific descriptions (Tufte 1983: 29). In 1765 Lambert described the use of a relational graph in which two variables (not including time) were plotted in relation to one another (Tilling 1975; Tufte 1983: 45). Today graphical representations are common and appear to us to be transparent representations, but the progression from spatial (cartographic) representations to time-based graphs and then to non-temporal non-spatial representations took place rather slowly. Some visual interfaces may seem strange initially, but if they are reasonably well designed, they will soon be treated as very natural and almost transparent, as we have all experienced with the computer desktop metaphor, which some users may even feel to be a direct representation of the workings of the personal computer.

 

In presenting the searches and graphical representations above, I may have given the impression that what I am proposing is a technology project, dealing essentially with technical or computational problems. This is not at all the case. I do not need time and space and a community to work out how to get a text to show hapax legomena in a red font. That is not something I have to grapple with. The problems to be faced are conceptual, not computational. What, for instance, is a text? How should a text be represented visually: as a two-dimensional physical object, perhaps, with representations of pages; or as a uni-dimensional object consisting of a string of characters; or as an abstract object viewed in terms of relations among words or relations among words and annotations? I have to evaluate the consequences of adopting different models and while I do not necessarily have to adopt a single representation of a text, I do have to assess the usefulness of visualisations based on different “views” of a text.

 

Another major conceptual problem is how to deal with different kinds of text mark-up. Humanities scholars have been heavily involved in the design and use of annotation schemes such as the Text Encoding Initiative and XML.[6] All digital texts contain some annotations and many contain numerous layers of mark-up. Clearly, a text analysis program must distinguish the mark-up from the text. But do we need to make a distinction between different types of mark-up? Should we try to distinguish mark-up that is representational from mark-up that is interpretive? The end result is obvious: a good text visualisation program should be able to work with a complex annotation scheme to provide the user with different views of a text on demand.

 

Notes

1. The version of this proposal posted here is now a slightly expanded version of the written proposal.

2. Bradley and Rockwell’s paper, in addition to describing themes in Hume’s dialogues, talks explicitly about the advantages of visualisation as an analytical tool. They state (p4):

Visualization is … more than the representation of texts. Textual visualisation tools use graphical displays to help the user explore a text or corpus. Unlike systems that produce graphs as a result of text analysis, visualization tools allow one to use texts to do the text analysis. A visualization tool helps the user query a corpus, refine the query and understand the results quickly.

3. One might argue that the increased use of heavily annotated texts in the humanities actually makes some kind of text visualisation software necessary just to read the (underlying) text.

4. Following McKinnon, Bradley and Rockwell use Correspondence Analysis to reduce word hyperspaces to produce “themes.” Cluett places different authors/texts within two coordinates based on linguistic features. Irizzary physically changes the text in various ways to bring out different features. Juillard and Luong use vector analysis to study similarities among a group of lexemes in a text. Olsen, noting (p312) that the “complexities of text and language have eluded simple measures based on word frequencies or content analysis”, suggests the adoption of a semiotic approach. See Henry (1993) for an alternative cognitive view.

Parunak displays lexical density scores at different locations. Galloway uses multidimensional scaling techniques to produce “maps” that represent the movement of the characters. Phelan looks at the distribution of selected words (such as pitee) within and across texts.In addition, Smith (1989) examines themes (based on fire and water) by examining the frequency of theme words (per 500 words) across a text and also by using Fourier analysis on the frequency distribution by using state diagrams to map the transitions between themes; and by using a three-D contour mapping system CGAMS to achieve a similar result. Ross (1989) discusses the ratio of different parts-of-speech revealed by regression analysis in poetry over several centuries (following Miles 1964).

5. A review of MonoConc is posted at http://linguistlist.org/issues/11/11-1411.html.

6. In particular, C. M. Sperberg-McQueen and Lou Burnard. The TEI homepage is at http://www.hcu.ox.ac.uk/TEI/.

 

 

Bibliography

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Barlow, Michael. 2000. MonoConc Pro (Concordance software). Houston: Athelstan.

Bradley, John and Geoffrey Rockwell. 1994. 'What Scientific Visualization Teaches us about Text Analysis.' Presented at the ALLC/ACH conference in Paris.

Bradley, John and Geoffrey Rockwell. 1996. 'Watching Scepticism: Computer Assisted Visualization and Hume’s Dialogues.' In Giorgio Perissinotto (ed), Research in Humanities Computing, 5. Clarendon Press: Oxford, 32-47.

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Irizarry, Estelle. 1996. 'Tampering with the Text to Increase Awareness of Poetry's Art.' Literary and Linguistic Computing 11, 4, 155-162.

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Parunak, H. Van Dyke. 1981. 'Prolegomena to Pictorial Concordances.' Computers and the Humanities 15, 15-36.

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